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Introduction to Python programming for data analysis. Learn pandas, numpy, and visualization libraries for data science.
Learn ChatGPT Complete Guide: Learn Midjourney, ChatGPT 4 & More
Deep Learning is a hot topic today! This is because of the impact it's having in several industries. One of fields in which deep learning has the most influence today is Computer Vision.Object detection, Image Segmentation, Image Classification, Image Generation & People Counting To understand why Deep Learning based Computer Vision is so popular; it suffices to take a look at the different domains where giving a computer the power to understand its surroundings via a camera has changed our lives.Some applications of Computer Vision are:Helping doctors more efficiently carry out medical diagnosticsenabling farmers to harvest their products with robots, with the need for very little human intervention,Enable self-driving carsHelping quick response surveillance with smart CCTV systems, as the cameras now have an eye and a brainCreation of art with GANs, VAEs, and Diffusion ModelsData analytics in sports, where players' movements are monitored automatically using sophisticated computer vision algorithms.The demand for Computer Vision engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn’t easy. There’s so much information out there, much of which is outdated and many times don't take the beginners into consideration :(In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step and project-based approach. You shall be using Tensorflow 2 (the world's most popular library for deep learning, built by Google) and Huggingface. We shall start by understanding how to build very simple mo
Complete PyTorch tutorial from basics to advanced topics. Learn tensors, autograd, neural networks, CNNs, RNNs, and more.
In this comprehensive course, you will master the fundamentals and advanced concepts of computer vision, focusing on Convolutional Neural Networks (CNN) and object detection models using TensorFlow and PyTorch. This course is designed to equip you with the skills required to build robust computer vision applications from scratch.What You Will LearnThroughout this course, you will gain expertise in:Introduction to Computer VisionUnderstanding image data and its structure.Exploring pixel values, channels, and color spaces.Learning about OpenCV for image manipulation and preprocessing.Deep Learning Fundamentals for Computer VisionIntroduction to Neural Networks and Deep Learning concepts.Understanding backpropagation and gradient descent.Key concepts like activation functions, loss functions, and optimization techniques.Convolutional Neural Networks (CNN)Introduction to CNN architecture and its components.Understanding convolution layers, pooling layers, and fully connected layers.Implementing CNN models using TensorFlow and PyTorch.Data Augmentation and PreprocessingTechniques for improving model performance through data augmentation.Using libraries like imgaug, Albumentations, and TensorFlow Data Pipeline.Transfer Learning for Computer VisionUtilizing pre-trained models such as ResNet, VGG, and EfficientNet.Fine-tuning and optimizing transfer learning models.Object Detection ModelsExploring object detection algorithms like:YOLO (You Only Look Once)Faster R-CNNImplement
This new course is the updated version of the previous course Deep Learning for Computer Vision with Tensorflow2.X.It contains new classes explaining in detail many state of the art algorithms for image classification and object detection.The course was entirely written using Google Colaboratory(Colab) in order to help students that don't have a GPU card in your local system, however you can follow the course easily if you have one.This time the course starts explaining in detail the building blocks from ConvNets which are the base for image classification and the base for the feature extractors in the latest object detection algorithms.We're going to study in detail the following concepts and algorithms:- Image Fundamentals in Computer Vision,- Load images in Generators with TensorFlow,- Convolution Operation,- Sparsity Connections and parameter sharing,- Depthwise separable convolution,- Padding,- Conv2D layer with Tensorflow,- Pooling layer,- Fully connected layer,- Batch Normalization,- ReLU activation and other functions,- Number of training parameters calculation,- Image Augmentation, etc- Different ConvNets architectures such as: * LeNet5, * AlexNet, * VGG-16, * ResNet, * Inception, * The lastest state of art Vision Transformer (ViT)- Many practical applications using famous datasets and sources such as: * Covid19 on X-Ray images, * CIFAR10, * Fashion MNIST, * BCCD, * COCO dataset, * Open Images Dataset V6 through Voxel Fifty
This course focuses on the fundamentals of Data Science, Machine learning, and deep learning in the beginning and with the passage of time, the content and lectures become advanced and more practical. But before everything, the introduction of python is discussed. Python is one of the fastest-growing programming languages and if we specifically look from the perspective of Data Science, Machine learning and deep learning, there is no other choice then “python” as a programming language.First of all, there is a crash course on python for those who are not very good with python and then there is an exercise for python that is supposed to be solved by you but if you feel any difficulty in solving the exercise, the solution is also provided.Then we moved on towards the Data Science and we start from data parsing using Scrapy then the data visualizations by using several libraries of python and finally we end up learning different data preprocessing techniques. And in the end, there is a complete project that we’ll do together.After that, we’ll be learning a few classical and a few advanced machine learning algorithms. Some of them will be implemented from scratch and the others will be implemented by using the builtin libraries of python. At the end of every algorithm, there will be a mini-project.Finally, Deep learning will be discussed, the basic structure of an artificial neural network and it’s the implementation in TensorFlow followed by a complete deep learning-based project. And in the end, some hyperparameter tuning techniques will be discussed that’ll improve the performance of the model.About The Instructor:Below is an introduction to Mr. Sajjad Mustafa, the instructor of this course.He an expert in Web
Welcome to the Advanced Machine Learning & Deep Learning Masterclass 2024! This comprehensive course is designed for both business professionals and researchers, offering over 24 hours of in-depth video content. Whether you're new to Python programming or experienced in the field, this course equips you with essential machine learning and deep learning techniques, from foundational Python skills to advanced neural network architectures.What You Will Learn:Python for Machine Learning: Set up the environment, use popular tools like Anaconda and PyCharm, and learn Python basics through step-by-step tutorials.Data Understanding & Preprocessing: Dive deep into statistical analysis, data pre-processing techniques, feature selection, and data visualization with Python.Artificial Neural Networks: Build neural networks from scratch, explore deep learning frameworks like Keras, and implement a full deep learning project on handwritten digit recognition.Advanced Deep Learning Mastery: Go beyond the basics with comprehensive modules on Convolutional Neural Networks (CNNs), transformers, large language models, and deep generative models. You'll learn how to construct and train models that power today’s AI innovations, including reinforcement learning and sequence models.Naive Bayes Classifier & NLP: Learn the fundamentals of Naive Bayes classification and explore natural language processing, including tokenization, part-of-speech tagging, and real-world NLP projects.Linear & Logistic Regression: Master regression models with hands-on demos for univariate and multivariate scenarios.With practical hands-on demos, coding exercises, and real-world proj
Have you ever watched AI automatically classify images or detect spam and thought, “I wish I could do that”? Have you ever wondered how a spam filter works? Or do you want to master Deep Learning in a hands-on way? With this course, you’ll learn how to build and deploy your own deep learning models in just 15 days - gaining practical, hands-on experience every step of the way.Why This Course?From day one, you’ll get comfortable with the essential concepts that power modern AI. No fluff, no endless theory - you'll learn by building real-world projects like Spam filters, or image detections. By the end, you won’t just know what neurons and neural networks are - you’ll be able to train, refine, and apply them to projects that truly matter.Who Is This Course For?Absolute beginners eager to break into the world of AI and deep learning.Data enthusiasts who want to strengthen their portfolios with hands-on projects.Developers and data scientists looking to deepen their PyTorch and model deployment skills.Anyone who craves a clear roadmap to mastering deep learning, one day at a time.What Makes This Course Unique?Day-by-Day Progression: Follow a structured, 15-day plan that ensures you never feel lost or overwhelmed.Real-World Projects: Predict used car prices, detect spam in SMS, classify handwritten digits, recognize fashion items—all using deep learning techniques.Modern Tools & Frameworks: Master industry-standard tools like PyTorch and dive into CNNs, transfer learning with ResNet, and more.Practical Deployment: Learn how to turn your trained models into interactive apps with Gradio, making your projects truly come alive.By the End of This Course, You Will:Confid
Deep learning has become one of the most popular machine learning techniques in recent years, and PyTorch has emerged as a powerful and flexible tool for building deep learning models. In this course, you will learn the fundamentals of deep learning and how to implement neural networks using PyTorch.Through a combination of lectures, hands-on coding sessions, and projects, you will gain a deep understanding of the theory behind deep learning techniques such as deep Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs). You will also learn how to train and evaluate these models using PyTorch, and how to optimize them using techniques such as stochastic gradient descent and backpropagation. During the course, I will also show you how you can use GPU instead of CPU and increase the performance of the deep learning calculation.In this course, I will teach you everything you need to start deep learning with PyTorch such as:NumPy Crash CoursePandas Crash CourseNeural Network Theory and IntuitionHow to Work with Torchvision datasetsConvolutional Neural Network (CNN)Long-Short Term Memory (LSTM)and much moreSince this course is designed for all levels (from beginner to advanced), we start with basic concepts and preliminary intuitions.By the end of this course, you will have a strong foundation in deep learning with PyTorch and be able to apply these techniques to various real-world problems, such as image classification, time series analysis, and even creating your own deep learning applications.
Welcome to the Full Stack Data Science & Machine Learning BootCamp Course, the only course you need to learn Foundation skills and get into data science.At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here's why:The course is taught by the lead instructor at the PwC, India's leading in-person programming bootcamp.In the course, you'll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix.This course doesn't cut any corners, there are beautiful animated explanation videos and real-world projects to build.The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback.To date, I’ve taught over 10000+ students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup.You'll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp.We'll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional.The course includes over 40+ hours of HD video tutorials and builds your programming knowledge while solving real-world problems.In the curriculum, we cover a large number of important data science and machine learning topics, such as:<
This course provides you to be able to build Deep Neural Networks models for different business domains with one of the most common machine learning library TensorFlow provided by Google AI team. The both concept of deep learning and its applications will be mentioned in this course. Also, we will focus on Keras. We will also focus on the advanced topics in this lecture such as transfer learning, autoencoders, face recognition (including those models: VGG-Face, Google FaceNet, OpenFace and Facebook DeepFace).This course appeals to ones who interested in Machine Learning, Data Science and AI. Also, you don't have to be attend any ML course before.
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Why this Course?Lot of us might have experienced difficulty when relating Machine Learning and Deep Learning models. This course aims to answer usual doubts such as,Why Deep Learning?Why Neural Network performs better than Machine Learning models?Deep Learning and Machine Learning are totally different technologies or they are much related?How Deep Learning evolved from Machine Learning?What it Covers?The course covers Machine Learning models such as Linear Regression, Perceptron, Logistic Regression and a Deep Learning model Dense Neural Network. The four chapters (videos) of the course deal with the adult life of a Legend named Mr. S and show how he used the Machine Learning and Deep Learning models to solve interesting problems such as partying, dating, searching for soulmate and eventually marrying the suitable girl in his life. Through the journey of Mr. S, you will finally get to know why Neural Network performs better & how Machine Learning and Deep Learning are related. Videos contain interesting scenarios with simple numerical examples and explanations.Who can opt for this Course?This course will be highly useful for those individuals,Who does/doesn't have CS background and wants to understand Deep Learning technically without coding & too much mathematics.Who are getting started with Machine Learning or Deep Learning.Who seeks the answer: Why Neural Network perform better than Machine Learning models and how Deep Learning evolved from Machine Learning.Who does research AI and have fundamental doubts about functionality of Neural Networks.
Welcome to "LLMs Mastery: Complete Guide to Generative AI & Transformers"!This practical course is designed to equip you with the knowledge and skills to build efficient, production-ready Large Language Models using cutting-edge technologies.Key Topics Covered:Generative AI: Understand the principles and applications of Generative AI in creating new data instances.ChatGPT & GPT4: Dive into the workings of advanced AI models like ChatGPT and GPT4.LLMs: Start with the basics of LLMs, learning how they decode, process inputs and outputs, and how they are taught to communicate effectively.Encoder-Decoders: Master the concept of encoder-decoder models in the context of Transformers.T5, GPT2, BERT: Get hands-on experience with popular Transformer models such as T5, GPT2, and BERT.Machine Learning & Data: Understand the role of machine learning and data in training robust AI models.Advanced Techniques: Sophisticated training strategies like PeFT, LoRa, managing data memory and merging adapters.Specialised Skills: Cutting-edge training techniques, including 8-bit, 4-bit training and Flash-Attention.Scalable Solutions: Master the use of advanced tools like DeepSpeed and FSDP to efficiently scale model training.Course Benefits:• Career Enhancement: Position yourself as a valuable asset in tech teams, capable of tackling significant AI challenges and projects.• <s
This is the most complete course online for learning about Python, Data Science, and Machine Learning. Join Jose Portilla's over 3 million students to learn about the future today!What is in the course?Welcome to the most complete course on learning Data Science and Machine Learning on the internet! After teaching over 2 million students I've worked for over a year to put together what I believe to be the best way to go from zero to hero for data science and machine learning in Python!This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Data Science and Machine Learning. The typical starting salary for a data scientists can be over $150,000 dollars, and we've created this course to help guide students to learning a set of skills to make them extremely hirable in today's workplace environment.We'll cover everything you need to know for the full data science and machine learning tech stack required at the world's top companies. Our students have gotten jobs at McKinsey, Facebook, Amazon, Google, Apple, Asana, and other top tech companies! We've structured the course using our experience teaching both online and in-person to deliver a clear and structured approach that will guide you through understanding not just how to use data science and machine learning libraries, but why we use them. This course is balanced between practical real world case studies and mathematical theory behind the machine learning algorithms.We cover advanced machine learning algorithms that most other courses don't! Including advanced regularization methods and state of the art unsupervised learning methods, such as DBSCAN.This comprehensive course is designed to be on par with Bootcamps that usually cost thousands
Are you interested in data science and machine learning, but you don't have any background, and you find the concepts confusing?Are you interested in programming in Python, but you always afraid of coding?I think this course is for you!Even if you are familiar with machine learning, this course can help you to review all the techniques and understand the concept behind each term.This course is completely categorized, and we don't start from the middle! We actually start from the concept of every term, and then we try to implement it in Python step by step. The structure of the course is as follows:Chapter1: Introduction and all required installationsChapter2: Useful Machine Learning libraries (NumPy, Pandas & Matplotlib)Chapter3: PreprocessingChapter4: Machine Learning TypesChapter5: Supervised Learning: ClassificationChapter6: Supervised Learning: RegressionChapter7: Unsupervised Learning: ClusteringChapter8: Model TuningFurthermore, you learn how to work with different real datasets and use them for developing your models. All the Python code templates that we write during the course together are available, and you can download them with the resource button of each section.Remember! That this course is created for you with any background as all the concepts will be explained from the basics! Also, the programming in Python will be explained from the basic coding, and you just need to know the syntax of Python.
Hello there,Welcome to the " Complete Data Science & Machine Learning A-Z with Python " CourseMachine Learning & Data Science all in one course with Python Data Visualization, Data Analysis Pandas & Numpy, KaggleMachine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning helps you stay ahead of new trends, technologies, and applications in this field.Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models. Python, machine learning, django, python programming, machine learning python, python for beginners, data science. Kaggle, statistics, r, python data science, deep learning, python programming, django, machine learning a-z, data scientist, python for data sciencePandas is an open source Python package that is most widely used for
Update 1.1 as of 29/01/2025Generative AILLMLangChainHuggingFaceOllamaOpenAIGeminiDeepSeekGoogle NotebookLMAzure AI Services (Azure OpenAI Service)Recent reviews: "Thorough explanation, going great so far. A very simplistic and straightforward introduction to Natural Language Processing. I will recommend this class to any one looking towards Data Science""This course so far is breaking down the content into smart bite-size pieces and the professor explains everything patiently and gives just enough background so that I do not feel lost.""This course is really good for me. it is easy to understand and it covers a wide range of NLP topics from the basics, machine learning to Deep Learning.The codes used is practical and useful.I definitely satisfy with the content and surely recommend to everyone who is interested in Natural Language Processing"Update 1.0 :Fasttext Library for Text classification section added.Hi Data Lovers,Do you have idea about Which Artificial Intelligence field is going to get big in upcoming year?According to statista dot com which field of AI is predicted to reach $43 billion by 2025?If answer is 'Natural Language Processing', You are at right place. Do you want to know How Google News classify millions of news article into hundreds of different category.How Android speech recognition recognize your voice with such high accuracy.How Google Translate actually translate hundreds of pairs of different languages into one
Understand ChatGPT, GPT, LLMs, Transformer Models and Generative AI concepts. Learn about prompt engineering.
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¡Descubre el fascinante mundo de ChatGPT y LLMs en acción! Domina desde cero hasta convertirte en un experto en el curso "Guía Completa de ChatGPT y LLMs en acción". Aprende a utilizar las poderosas herramientas de LLMs y ChatGPT para impulsar tu conocimiento. En un mundo cada vez más impulsado por la inteligencia artificial, los Modelos de Lenguaje Grande (LLMs) desempeñan un papel fundamental en la sociedad. Estos modelos revolucionarios tienen el potencial de transformar la forma en que interactuamos con la tecnología, desde asistentes virtuales hasta chatbots y sistemas de recomendación personalizados. Dominar los LLMs te permitirá impulsar la innovación, crear soluciones inteligentes y satisfacer las crecientes demandas de una sociedad cada vez más conectada. Con este curso, estarás preparado para marcar la diferencia en el mundo de la inteligencia artificial generativa y aprovechar al máximo el potencial que ofrecen los LLMs en la sociedad, trabajo y vida diaria.¿Que aprenderás en el curso?Conviértete en un experto en modelos de lenguaje: Aprende desde los conceptos básicos hasta las técnicas avanzadas para aprovechar al máximo los LLMs y transforma tus habilidades en el campo de la inteligencia artificial generativa.Aprende a utilizar herramientas clave: Descubre las herramientas esenciales, como la API de Open AI, Hugging Face y LangChain, y domina su integración en tus proyectos de LLMs y NLP.Mejora el rendimiento de tus modelos: Domina el Prompt Engineering y obtén resultados óptimos en tus interacciones con ChatGPT. Aprende a seleccionar el modelo adecuado y a aplicar técnicas de transfer learning para potenciar tus proyectos.Amplía las capacidades de ChatGPT y LangChain con LangChain y Agentes: Descubre cómo encadenar varios LLMs y dale nuevas habilidades a tus modelos. Aprende a programar workflows y a i
Welcome to "ChatGPT Unleashed: Harnessing the Potential of Generative AI," an exciting online course designed to empower students with the knowledge and skills needed to conquer the world of Artificial Intelligence! With over 165+ prompts! Are you fascinated by the transformative capabilities of AI, specifically ChatGPT? This course is your gateway to unlocking the immense potential of Generative AI. Whether you're a curious student, a tech enthusiast, or a professional looking to stay ahead in your field, this program is tailored just for you.This is a practical course, not just theory. You will do many ChatGPT prompt examples while watching the instructor provide his own examples for you to follow!THIS COURSE INCLUDES:30 beginner ChatGPT prompts (template)45 intermediate-level prompts (template)50 advanced prompts (template)25 "Make money online" prompts (template)15+ LIVE instructor-led examples30 day money back guaranteeCourse certificateBONUS: Learn how to cartoon yourself with a GPT!HERE'S WHAT YOU CAN EXPECT:Foundations of Generative AI: We'll start by building a solid foundation in Generative AI and its role in shaping the future.Real life examples of how to use ChatGPT for your personal and business needs.Prompt Engineering Mastery: Learn the secrets to creating prompts that produce precise and valuable AI-generated responses.Hands-On Projects: Put your knowledge to the test with ha
Hello there,Welcome to the “Machine Learning & Data Science with Python & Kaggle | A-Z” course.Data Science & Machine Learning A-Z & Kaggle with Heart Attack Prediction projects and Machine Learning Python projectsMachine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning helps you stay ahead of new trends, technologies, and applications in this field.Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models. Python, machine learning, django, python programming, machine learning python, python for beginners, data science. Kaggle, statistics, r, python data science, deep learning, python programming, django, machine learning a-z, data scientist, python for data sciencePython instructors on OAK Academy specialize in everything from software d
Learning how to program in Python is not always easy especially if you want to use it for Data science. Indeed, there are many of different tools that have to be learned to be able to properly use Python for Data science and machine learning and each of those tools is not always easy to learn. But, this course will give all the basics you need no matter for what objective you want to use it so if you :- Are a student and want to improve your programming skills and want to learn new utilities on how to use Python- Need to learn basics of Data science- Have to understand basic Data science tools to improve your career- Simply acquire the skills for personal useThen you will definitely love this course. Not only you will learn all the tools that are used for Data science but you will also improve your Python knowledge and learn to use those tools to be able to visualize your projects.The structure of the courseThis course is structured in a way that you will be able to to learn each tool separately and practice by programming in python directly with the use of those tools. Indeed, you will at first learn all the mathematics that are associated with Data science. This means that you will have a complete introduction to the majority of important statistical formulas and functions that exist. You will also learn how to set up and use Jupyter as well as Pycharm to write your Python code. After, you are going to learn different Python libraries that exist and how to use them properly. Here you will learn tools such as NumPy or SciPy and many others. Finally, you will have an introduction to machine learning and learn how a machine learning algorithm works. All this in just one course.Another very interesting thing about this course it contains a lot of practice. Indeed, I build all my course on a concept of learning by practice. In other words, this course contains a lot of practice this way you will be able to be sure that you completely underst
Just launched with all modern best practices for building neural networks with TensorFlow and becoming a TensorFlow & Deep Learning Expert!Join a live online community of over 900,000+ students and a course taught by a TensorFlow expert. This course will take you from absolute beginner with TensorFlow, to creating state-of-the-art deep learning neural networks.TensorFlow experts earn up to $204,000 USD a year, with the average salary hovering around $148,000 USD. By taking this course you will be joining the growing Machine Learning industry and becoming a top paid TensorFlow Developer!Here is a full course breakdown of everything we will teach (yes, it's very comprehensive, but don't be intimidated, as we will teach you everything from scratch!):The goal of this course is to teach you all the skills necessary for you to become a top 10% TensorFlow Developer.This course will be very hands on and project based. You won't just be staring at us teach, but you will actually get to experiment, do exercises, and build machine learning models and projects to mimic real life scenarios. By the end of it all, you will develop skillsets needed to develop modern deep learning solutions that big tech companies encounter. 0 — TensorFlow FundamentalsIntroduction to tensors (creating tensors)Getting information from tensors (tensor attributes)Manipulating tensors (tensor operations)Tensors and NumPyUsing @tf.function (a way to speed up your regular Python functions)Using GPUs with TensorFlow1 — Neural Network Regression with TensorFlowBuild TensorFlow sequential models with multiple layersPrepare data for use with a machine learning model
Hello there,Welcome to the “Complete Machine Learning & Data Science with Python | A-Z” course Use Scikit, learn NumPy, Pandas, Matplotlib, Seaborn, and dive into machine learning A-Z with Python and Data Science Machine learning isn’t just useful for predictive texting or smartphone voice recognition Machine learning is constantly being applied to new industries and new problems Whether you’re a marketer, video game designer, or programmer, my course on OAK Academy here to help you apply machine learning to your work Complete machine learning & data science with python | a-z, machine learning a-z, Complete machine learning & data science with python, complete machine learning and data science with python a-z, machine learning using python, complete machine learning and data science, machine learning, complete machine learning, data scienceIt’s hard to imagine our lives without machine learning Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models Python, machine learning, django, python programming, machine learning python, python for beginners, data sciencePython instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn Python's simple syntax is especially suited for desktop, web, and business applications Python's design philosophy emphasizes readability and usability Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization The core programming language is quite small
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Are you ready to master the most disruptive technology of our time?The landscape of Artificial Intelligence is evolving rapidly, and Generative AI is no longer just a buzzword—it is a mandatory career skill. Welcome to the Generative AI, ChatGPT & AI Agents Masterclass (2026 Edition), the only course you need to transition from an AI beginner to an industry-ready expert.This course is built for the future. While others focus on outdated tactics, we dive deep into the next generation of Large Language Models (LLMs), preparing you specifically for the capabilities of ChatGPT-5 and beyond.We don't just talk theory; we build. You will learn to:Dominate Prompt Engineering to get precise results every time.Accelerate your coding and development speed by 10x using GitHub Copilot.Design and deploy autonomous AI Agents that can plan, execute, and complete complex workflows for you.Whether you are a developer, entrepreneur, or creative professional, mastering these tools is the key to future-proofing your career. Join us to unlock the full potential of Generative AI and stay ahead of the curve in 2026.In this comprehensive course, you will learn to instantly create:Content & Marketing: SEO articles, video scripts, targeted ads, social media posts, E-books, blogs, newsletters, and E-commerce copy.Business & Productivity: AI integrations, workflow automations, financial plans, business plans, project outlines, custom schedules, contracts, and company slogans.Creative & Learning: Personalized emails, job proposals, presentations, online courses, lesson plans, language translations, creative stories, and even "vibe code" websites from scratch with no co
Do you want to master NumPy and unlock your potential in data science? This course is your comprehensive, hands-on introduction to the foundational library of modern Python computing!NumPy is the absolute core building block for essential data science and machine learning libraries like Pandas, Scikit-learn, and PyTorch. By mastering it, you gain the technical edge needed for advanced topics like linear algebra, image processing, and fast numerical computations. If you want to start a career in Data Science or understand the engine behind Machine Learning in Python, this course is for you.What You'll Master in this Hands-On Python Course:This course will teach you everything you need to professionally use NumPy for scientific computing. We start with the basics and rapidly move into advanced techniques crucial for complex data science tasks.Foundation: Introduction to NumPy arrays, N-dimensional arrays, and the fundamental concepts of vectors and matrices.Data Analysis Tools: Leverage Universal Functions (ufuncs), Randomness, and Statistics to analyze and explore data efficiently in Python.Linear Algebra for ML: Master Basic and Advanced Linear Algebra operations, which are the backbone of all Machine Learning algorithms.Advanced Techniques: Understand Broadcasting and Advanced Indexing to write fast, memory-efficient Python code.Real-World Scientif
Hello there,Machine learning python, python, machine learning, django, ethical hacking, python bootcamp, data analysis, machine learning python, python for beginners, data science, machine learning, djangoWelcome to the “Machine Learning and Deep Learning A-Z: Hands-On Python ” course Python Machine Learning and Python Deep Algorithms in Python Code templates included Python in Data Science | 2021Do you know data science needs will create 11 5 million job openings by 2026?Do you know the average salary is $100 000 for data science careers!Deep learning a-z, machine learning a-z, deep learning, machine learning, machine learning & data science a-z: hands on python 2021, machine learning python, machine learning python, machine learning algorithms, python, Itsm, machine learning and deep learning a-z: hands on python, machine learning and deep learning a-z hands pn python, data science, rnn, deep learning python, data science a-z, recurrent neural network,Machine learning isn’t just useful for predictive texting or smartphone voice recognition Machine learning is constantly being applied to new industries and new problems Whether you’re a marketer, video game designer, or programmer, my course on Udemy here to help you apply machine learning to your work Data Science Careers Are Shaping The FutureData science experts are needed in almost every field, from government security to dating apps Millions of businesses and government departments rely on big data to succeed and better serve their customers So data science careers are in high demandUdemy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies Whether you’re interested in machine learning, data mining, or data analysis, Udemy has a course for you<li
A comprehensive course on building, deploying, and optimizing AI models using Langchain and Hugging Face. It covers everything from the basics of Generative AI to advanced concepts like Retrieval-Augmented Generation (RAG) pipelines.
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Are you looking to gain in-depth knowledge of machine learning and deep learning? If yes, then this Learning Path just right for you. Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. R is one of the leading technologies in the field of data science. Starting out at a basic level, this Learning Path will teach you how to develop and implement machine learning and deep learning algorithms using R in real-world scenarios. The Learning Path begins with covering some basic concepts of R to refresh your knowledge of R before we deep-dive into the advanced techniques. You will start with setting up the environment and then perform data ETL in R. You will then learn important machine learning topics, including data classification, regression, clustering, association rule mining, and dimensionality reduction. Next, you will understand the basics of deep learning and artificial neural networks and then move on to exploring topics such as ANNs, RNNs, and CNNs. Finally, you will learn about the applications of deep learning in various fields and understand the practical implementations of scalability, HPC, and feature engineering. By the end of the Learning Path, you will have a solid knowledge of all these algorithms and techniques and be able to implement them efficiently in your data science projects. Do not worry if this seems too far-fetched right now; we have combined the best works of the following esteemed authors to ensure that your learning journey is smooth: About the Authors Selva Prabhakaran is a data scientist with a large e-commerce organization. In his 7 years of experience in data science, he has tackled comp
This course provides a comprehensive, hands-on introduction to machine learning on the Google Cloud Platform, with a specific focus on Vertex AI. Students will learn about various GCP services, including compute, storage, and databases, before diving into machine learning workflows. The curriculum covers building and deploying models using GCP's AutoML for tabular, image, and text data, as well as custom model training and deployment on the AI Platform and Vertex AI. The course is designed to equip learners with the practical skills needed to create and manage machine learning pipelines on Google Cloud.
Are you ready to unlock the full potential of Deep Learning and AI by mastering not just one but multiple tools and frameworks? This comprehensive course will guide you through the essentials of Deep Learning using Python, PyTorch, and TensorFlow—the most powerful libraries and frameworks for building intelligent models.Whether you're a beginner or an experienced developer, this course offers a step-by-step learning experience that combines theoretical concepts with practical hands-on coding. By the end of this journey, you'll have developed a deep understanding of neural networks, gained proficiency in applying Deep Neural Networks (DNNs) to solve real-world problems, and built expertise in cutting-edge deep learning applications like Convolutional Neural Networks (CNNs) and brain tumor detection from MRI images.Why Choose This Course?This course stands out by offering a comprehensive learning path that merges essential aspects from three leading frameworks: Python, PyTorch, and TensorFlow. With a strong emphasis on hands-on practice and real-world applications, you'll quickly advance from fundamental concepts to mastering deep learning techniques, culminating in the creation of sophisticated AI models.Key Highlights:Python: Learn Python from the basics, progressing to advanced-level programming essential for implementing deep learning algorithms.PyTorch: Master PyTorch for neural networks, including tensor operations, optimization, autograd, and CNNs for image recognition tasks.TensorFlow: Unlock TensorFlow's potential for creating robust deep learning models, utilizing tools like Tensorboard for model visualization.Real-world Projects: Apply your knowledge to exciting projects like IRIS classi
Welcome to this comprehensive hands-on course on YOLOv10 for real-time object detection! YOLOv10 is the latest version in the YOLO family, building on the successes and lessons from previous versions to provide the best performance yet. This course is designed to take you from beginner to proficient in using YOLOv10 for various object detection tasks.Throughout the course, you will learn how to set up and use YOLOv10, label and create datasets, and train the model with custom data. The course is divided into three main parts:Part 1: Learning to Use YOLOv10 with Pre-trained Models In this section, we will start by setting up our environment using Google Colab, a free cloud-based platform with GPU support. You will learn to download and use pre-trained YOLOv10 models to detect objects in images. We will cover the following:Setting up the environment and installing necessary packages.Downloading pre-trained YOLOv10 models.Performing object detection on sample images.Visualizing and interpreting detection results.Part 2: Labeling and Making a Dataset with RoboFlowIn the second part, we will focus on creating and managing custom datasets using RoboFlow. This section will teach you how to:Create a project workspace on the RoboFlow website.Upload and annotate images accurately.Follow best practices for data labeling to ensure high-quality training results.Export labeled datasets in formats compatible with YOLOv10.Part 3: Training with Custom DatasetsThe final section of the course is dedicated to training YOLOv10 with your custom datasets. You will learn how to:Configure the training process, including setting parameters such as epochs and batch size.Train the YOLOv10 model using your labeled dataset from RoboFlow.Monitor training progress and evaluate the trained model.
This course teaches big ideas in machine learning like how to build and evaluate predictive models. This course provides an intro to clustering in R from a machine learning perspective.This online machine learning course is perfect for those who have a solid basis in R and statistics but are complete beginners with machine learning. You’ll get your first intro to machine learning.After learning the true fundamentals of machine learning, you'll experiment with the techniques that are explained in more detail. By the end, you'll be able to learn and build a decision tree and to classify unseen observations with k-Nearest Neighbors.Also, you'll be acquainted with simple linear regression, multi-linear regression, and k-Nearest Neighbors regression.This course teaches the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for optimal performance, how to preprocess data for better results, and much more.At the end of this course, our machine learning and data science video tutorials, you’ll have a great understanding of all the main principles.Details of the course:Module 01: Basics of R toolIn this video, we are going to install r programming with rstudio in Windows Platform.Lab 01 R Installation and ConceptsIn this lab, we are going to learn about how we can install R Programing in Windows and learn about its several key concepts that are necessary for Programming in R.Video 2_R Programming ConceptsIn this video, we are going to learn the necessary concepts of RProgramming.Video 3_R Progrming ComputationsIn this tutorial, we will be learning about several mathematical algorithms and computations.Lab 02 R P
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Hello there,Welcome to the “Python Programming: Machine Learning, Deep Learning | Python” coursePython, machine learning, python programming, django, ethical hacking, data analysis, python for beginners, machine learning python, python bootcampPython Machine Learning and Python Deep Learning with Data Analysis, Artificial Intelligence, OOP, and Python ProjectsComplete hands-on deep learning tutorial with Python Learn Machine Learning Python, go from zero to hero in Python 3Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn Python's simple syntax is especially suited for desktop, web, and business applications Python's design philosophy emphasizes readability and usability Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization The core programming language is quite small and the standard library is also large In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks Machine learning isn’t just useful for predictive texting or smartphone voice recognition Machine learning is constantly being applied to new industries and new problems Whether you’re a marketer, video game designer, or programmer, this course is here to help you apply machine learning to your work It’s hard to imagine our lives without machine learning Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathe
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Practical machine learning tutorial series using Python. Covers regression, classification, clustering, neural networks with hands-on coding examples.
Python, Java, PyCharm, Android Studio and MNIST. Learn to code and build apps! Use machine learning models in hands-on projects. A wildly successful Kickstarter funded this courseExplore machine learning concepts. Learn how to use TensorFlow 1.4.1 to build, train, and test machine learning models. We explore Python 3.6.2 and Java 8 languages, and how to use PyCharm 2017.2.3 and Android Studio 3 to build apps. A machine learning framework for everyoneIf you want to build sophisticated and intelligent mobile apps or simply want to know more about how machine learning works in a mobile environment, this course is for you.Be one of the firstThere are next to no courses on big platforms that focus on mobile machine learning in particular. All of them focus specifically on machine learning for a desktop or laptop environment.We provide clear, concise explanations at each step along the way so that viewers can not only replicate, but also understand and expand upon what I teach. Other courses don’t do a great job of explaining exactly what is going on at each step in the process and why we choose to build models the way we do. No prior knowledge is requiredWe will teach you all you need to know about the languages, software and technologies we use. If you have lots of experience building machine learning apps, you may find this course a little slow because it’s designed for beginners.Jump into a field that has more demand than supplyMachine learning changes everything. It’s bringing us self-driving cars, facial recognition and artificial intelligence. And the best part is: anyone can create such innovations."This course is GREA
Welcome to the Complete Guide to TensorFlow for Deep Learning with Python! This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way! This course covers a variety of topics, including Neural Network BasicsTensorFlow BasicsArtificial Neural NetworksDensely Connected NetworksConvolutional Neural NetworksRecurrent Neural NetworksAutoEncodersReinforcement LearningOpenAI Gymand much more! There are many Deep Learning Frameworks out there, so why use TensorFlow? TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system
Large Language Models have revolutionized the field of natural language processing, enabling breakthroughs in tasks such as text generation, language understanding, and content summarization. In this course, you will embark on a journey through the principles, techniques, and applications of LLMs, guided by experts in the field.Unlock the potential of advanced language technologies by enrolling in our comprehensive course, "Building Powerful Language Model Applications with LangChain." In this dynamic and hands-on learning experience, you will delve into the world of large language models and discover how to leverage the capabilities of LangChain, a cutting-edge framework designed to develop, fine-tune, and deploy robust language models.Language models have revolutionized the way we interact with technology, enabling applications such as chatbots, language translation, text generation, and sentiment analysis. This course is tailored for individuals seeking to harness the full potential of language models for real-world applications, whether you're a seasoned developer or an aspiring AI enthusiast.Course Highlights:Introduction to LangChain: Gain a solid understanding of the LangChain framework, exploring its architecture, features, and advantages over traditional language model development approaches.Fundamentals of Large Language Models: Learn the underlying principles and theories behind large language models, including transformer architectures, pre-training, and fine-tuning techniques.Data Preparation and Preprocessing: Master the art of curating and preprocessing datasets for effective language model training, ensuring data quality and model performance.Model Development and Training: Dive into the process of designing, building, and training your own large language model application using LangChain. Explore strategies for mode
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Learn Python for Data Science & Machine Learning from A-ZIn this practical, hands-on course you’ll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner. Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job.We'll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib +NumPy — A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.Pandas — A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you’d find in Excel or Google Sheets. Blending practical work with solid theoretical training, we take you from the basics of Python Programming for Data Science to mastery.This Machine Learning with Python course dives into the basics of machine learning using Python. You'll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.We understand that theory is important to build a solid foundation, we understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the Python programming language, this course is for you!Python coding experience
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Interested in the field of Machine Learning? Then this course is for you!Designed & Crafted by AI Solution Expert with 15 + years of relevant and hands on experience into Training , Coaching and Development.Complete Hands-on AI Model Development with Python. Course Contents are:Understand Machine Learning in depth and in simple process. Fundamentals of Machine LearningUnderstand the Deep Learning Neural Nets with Practical Examples.Understand Image Recognition and Auto Encoders.Machine learning project Life CycleSupervised & Unsupervised LearningData Pre-ProcessingAlgorithm SelectionData Sampling and Cross ValidationFeature EngineeringModel Training and ValidationK -Nearest Neighbor AlgorithmK- Means AlgorithmAccuracy DeterminationVisualization using SeabornYou will be trained to develop various algorithms for supervised & unsupervised methods such as KNN , K-Means , Random Forest, XGBoost model development. Understanding the fundamentals and core concepts of machine learning model building process with validation and accuracy metric calculation. Determining the optimum model and algorithm. Cross validation and sampling methods would be understood. Data processing concepts with practical guidance and code examples provided through the course. Feature Engineering as critical machine learning process would be explained in easy to understand and yet effective manner.We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
Welcome to the Learn Data Science and Machine Learning with R from A-Z Course!In this practical, hands-on course you’ll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.Our main objective is to give you the education not just to understand the ins and outs of the R programming language, but also to learn exactly how to become a professional Data Scientist with R and land your first job.The course covers practical issues in statistical computing which include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Blending practical work with solid theoretical training, we take you from the basics of R Programming to mastery.We understand that theory is important to build a solid foundation, we understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the R programming language, this course is for you!R coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers and much more. Adding R coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.Together we’re going to give you the foundational education that you need to know not just on how to write code in R, analyze and visualize data but also how to get paid for your newly developed programming skills.The cour
Unlock the power of Artificial Intelligence, Python, Machine Learning, Data Science, and Big Data Analytics in this comprehensive, hands-on course. Whether you’re a beginner or an aspiring data professional, this course equips you with the practical skills and knowledge to solve real-world problems using cutting-edge technologies.What You Will Learn:Fundamentals of Python programming for AI and data analysisBuilding and deploying Machine Learning models from scratchExploring Data Science techniques, including data cleaning, visualization, and analysisWorking with Big Data Analytics tools to handle massive datasetsImplementing AI solutions for real-world projects and business applicationsUnderstanding key concepts in Deep Learning, Neural Networks, and Predictive AnalyticsWho This Course is For:Anyone passionate about leveraging AI and Big Data to make smarter decisionsWhy Choose This Course:Hands-on projects and real-world examplesLearn from beginner-friendly to advanced concepts in a structured wayFocused on practical applications that can boost your career or businessCertificate after course completeBy the end of this course, you will have the confidence and skills to design and implement AI-powered solutions, build machine learning models, analyze complex datasets, and tackle big data challenges.Start your journey to becoming an AI, Machine Learning, and Data Science expert today!
This Data Science Course is a comprehensive program designed to provide learners with the essential skills and knowledge needed to understand, analyze, and apply data-driven solutions in the modern world. This course is carefully structured to take you from the basics of data handling to advanced concepts in data analysis, visualization, and predictive modeling.Beginning with fundamental programming skills in Python and R, the course introduces you to core topics such as data cleaning, exploratory data analysis, and statistical methods. From there, you’ll gain hands-on experience with popular tools and libraries, including Pandas, NumPy, Matplotlib, and Scikit-learn, to manipulate and visualize data effectively. Machine learning concepts are introduced gradually, ensuring a strong foundation in supervised and unsupervised learning, model building, and evaluation techniques.Beyond technical skills, this course emphasizes practical, real-world applications of data science across industries such as business, healthcare, finance, and technology. Through projects and case studies, you will develop the ability to extract actionable insights, solve complex problems, and communicate findings clearly to both technical and non-technical audiences.By the end of the program, you will not only understand the theory but also be confident in applying data science methods to real datasets. Whether you are a student, professional, or aspiring data scientist, the Data Science Course by Shimwa Bonheur equips you with the tools and confidence to thrive in today’s data-driven world.
Welcome to the comprehensive course on Predictive Analysis and Machine Learning Techniques! In this course, you will embark on a journey through various aspects of predictive analysis, from fundamental concepts to advanced machine learning algorithms. Whether you're a beginner or an experienced data scientist, this course is designed to provide you with the knowledge and skills needed to tackle real-world predictive modeling challenges.Through a combination of theoretical explanations, hands-on coding exercises, and practical examples, you will gain a deep understanding of predictive analysis techniques and their applications. By the end of this course, you'll be equipped with the tools to build predictive models, evaluate their performance, and extract meaningful insights from data.Join us as we explore the fascinating world of predictive analysis and unleash the power of data to make informed decisions and drive actionable insights!Section 1: Introduction This section serves as an introduction to predictive analysis, starting with an overview of Java Netbeans. Students will understand the basics of predictive modeling and explore algorithms like random forest and extremely random forest, laying the groundwork for more advanced topics in subsequent sections.Section 2: Class Imbalance and Grid Search Here, students delve into more specialized topics within predictive analysis. They learn techniques for addressing class imbalance in datasets, a common challenge in machine learning. Additionally, they explore grid search, a method for systematically tuning hyperparameters to optimize model performance.Section 3: Adaboost Regressor The focus shifts to regression analysis with the Adaboost algorithm. Students understand how Adaboost works and apply it to predict traffic patterns, gaining practical experience in regression modeling.Section 4: Detecting Patterns with Unsupervised Learning</strong
You’ve just stumbled upon the most complete, in-depth Computer Vision course online.Whether you want to:- build the skills you need to get your first Computer Vision programming job- move to a more senior software developer position- become a computer scientist mastering in computation- or just learn Computer Vision to be able to work with your own projects quickly....this complete Computer Vision Masterclass is the course you need to do all of this, and more.This course is designed to give you the Computer Vision skills you need to become a Computer Vision expert. By the end of the course, you will understand Computer Vision extremely well and be able to work with your own Computer Vision projects and be productive as a computer scientist and software developer.What makes this course a bestseller?Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.Like you, they were tired of low-quality lessons, poorly explained topics, and confusing info presented in the wrong way. That’s why so many find success in this complete Computer Vision course. It’s designed with simplicity and seamless progression in mind through its content.This course assumes no previous coding experience and takes you from absolute beginner core concepts. You will learn the core Computer Vision skills and master logic programming. It's a one-stop shop to learn Computer Vision. If you want to go beyond the core content you can do so at any time.Here’s just some of what you’ll learn(It’s okay if you don’t understand all this yet, you will in the course)Understand the formation mechanisms of Digital Images and the
Growing Importance of Deep LearningDeep learning underpins a lot of important and increasingly important applications today ranging from facial recognition, to self-driving cars, to medical diagnostics and more. Made for Anyone Although many courses are very mathematical or too practical in nature, this course strikes a careful balance between the two to provide a solid foundation in deep learning for you to explore further if you are interested in research in the field of deep learning and/or applied deep learning. It is purposefully made for anyone without a strong background in mathematics. And for those with a strong background, it would accelerate your learning in understanding the different models in deep learning. Code As You Learn This entire course is delivered in a Python Notebook such that you can follow along the videos and replicate the results. You can practice and tweak the models until you truly understand every line of code as we go along. I highly recommend you to type every line of code when you are listening to the videos as this will help a lot in getting used to the syntax. Gradual Learning Style The thing about many guides out there is that they lack the transition from the very basics and people often get lost or miss out vital links that are critical in understanding certain models. Because of this, you can see how every single topic is closely linked with one another. In fact, at the beginning of every topic from logistic regression, I take the time to carefully explain how one model is simply a modification from the previous. That is the marvel of deep learning, we can trace back some part of it to linear regression where we will start. Diagram-Driven Code This course uses more than 100 custom-made diagrams where I took hundreds of hours to carefu
Comprehensive ML course covering regression, classification, clustering, deep learning, NLP, reinforcement learning.
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You want to be able to perform your own data analyses with R? You want to learn how to get business-critical insights out of your data? Or you want to get a job in this amazing field? In all of these cases, you found the right course!We will start with the very Basics of R, like data types and -structures, programming of loops and functions, data im- and export.Then we will dive deeper into data analysis: we will learn how to manipulate data by filtering, aggregating results, reshaping data, set operations, and joining datasets. We will discover different visualisation techniques for presenting complex data. Furthermore find out to present interactive timeseries data, or interactive geospatial data.Advanced data manipulation techniques are covered, e.g. outlier detection, missing data handling, and regular expressions.We will cover all fields of Machine Learning: Regression and Classification techniques, Clustering, Association Rules, Reinforcement Learning, and, possibly most importantly, Deep Learning for Regression, Classification, Convolutional Neural Networks, Autoencoders, Recurrent Neural Networks, ...You will also learn to develop web applications and how to deploy them with R/Shiny.For each field, different algorithms are shown in detail: their core concepts are presented in 101 sessions. Here, you will understand how the algorithm works. Then we implement it together in lab sessions. We develop code, before I encourage you to work on exercise on your own, before you watch my solution examples. With this knowledge you can clearly identify a problem at hand and develop a plan of attack to solve it.You will understand the advantages and disadvantages of d
Machine learning has become one of the most common practices used by many organizations, groups and individuals. It helps various software to predict the outcome more precisely without any programming. Machine learning finds the pattern in the input data and uses statistical analysis to foretell the result. To support its extensive requirements, Tensorflow was launched by Google. In order to provide next-generation machine learning solutions, we have hand-picked this course covering all its aspects. Why this course is important? Machine learning often requires heavy computation and for that Tensorflow was developed as an open source library. Tensorflow not only does the heavy computation but can also build dataflows. Apart from machine learning, it is also used in wide variety of other domains by the experts. This course contains different topics to make you understand everything about next-generation machine learning by Tensorflow. What makes this course so valuable? It includes all the basics of Tensorflow with detail description of tensors, operators and variables. Installation of Tensorflow on Windows, Mac and Linux is clearly shown. Additionally, it gives insights into the basics of machine learning and its types. This course also covers various algorithms like linear regression, logistic regression, NN regression, K-Means algorithm and others. Herein, advanced machine learning is also well elaborated with the topics of neural networks, convolution neural networks, recurrent neural networks and so on. This course includes- 1.Tensorflow fundamentals and installation 2. Details about tensors, operators, variables and others 3. Details about machine learning, inference and its types 4. Different algorithms like linear regression, logistic regression, clustering, K-means algorithm, kernels and many more 5. Various advanced learning networks and its implementation - Neural Networks, Conv
USED BY SOFTWARE STUDENTS AT CAMBRIDGE UNIVERSITY - WORLD CLASS DEEP LEARNING COURSE - UPDATED CONTENT January 2018 Master practical deep learning and neural network concepts and fundamentals My course does exactly what the title describes in a simple, relatable way. I help you to grasp the complete start to end concepts of fundamental deep learning. Why you need this course Coming to grips with python isn't always easy. On your own it can be quite confusing, difficult and frustrating. I've been through the process myself, and with the help of lifelong ... I want to share this with my fellow beginners, developers, AI aspirers, with you. What you will get out of this course I will give you straightforward examples, instructions, advice, insights and resources for you to take simple steps to create your own neural networks from scratch. By the end of the course you will be able to create neural networks to create your very own image classifier, able to work on your own images. I personally provide support within the course, answering questions and giving feedback on what you're discovering/creating along the way. I don't just throw you in at the deep end - I provide you with the resources to learn and develop what you need at a pace to work for you and then help you stroll through to the finish line. Studies have shown that to learn effectively from online courses tutorials should last around ten minutes each. Therefore to maximise your learning experience all of the lectures in this course have been created around this amount of time. My course integrates all of the aspects required to get you on the road becoming a successful deep learning developer. I teach and I preach, with live, practical exercises and walkthroughs at the end of each section!
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Unlock the power of artificial intelligence with our comprehensive course, "Deep Learning with Python ." This course is designed to transform your understanding of machine learning and take you on a journey into the world of deep learning. Whether you're a beginner or an experienced programmer, this course will equip you with the essential skills and knowledge to build, train, and deploy deep learning models using Python and PyTorch. Deep learning is the driving force behind groundbreaking advancements in generative AI, robotics, natural language processing, image recognition, and artificial intelligence. By enrolling in this course, you’ll gain practical knowledge and hands-on experience in applying Python skills to deep learningCourse OutlineIntroduction to Deep Learning Understanding the paradigm shift from machine learning to deep learningKey concepts of deep learningSetting up the Python environment for deep learningArtificial Deep Neural Networks: Coding from Scratch in PythonFundamentals of artificial neural networksBuilding and training neural networks from scratchImplementing forward and backward propagationOptimizing neural networks with gradient descentDeep Convolutional Neural Networks: Coding from Scratch in PythonIntroduction to convolutional neural networks (CNNs)Building and training CNNs from scratchUnderstanding convolutional layers, pooling, and activation functionsApplying CNNs to image dataTransfer Learning with Deep Pretrained Models using PythonConcept of transfer learning and its benefitsUsing pretrained models for new tasksFine-tuning and adapting pretrained modelsPractical applications of
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This course provides a comprehensive introduction to attention mechanisms and the transformer models that are foundational to modern GenAI systems. It covers self-attention, multi-head attention, and the overall transformer architecture, with real-world demos.
Clear and simple explanations of machine learning algorithms. Understand the math and intuition behind ML with Josh Starmer.
Deep Learning is one of the most popular fields in computer science today. It has applications in many and very varied domains. With the publishing of much more efficient deep learning models in the early 2010s, we have seen a great improvement in the state of the art in domains like Computer Vision, Natural Language Processing, Image Generation, and Signal Processing. The demand for Deep Learning engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn’t easy. There’s so much information out there, much of which is outdated and many times don't take the beginners into consideration :(In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step and project-based approach. You shall be using Tensorflow 2 (the world's most popular library for deep learning, and built by Google) and Huggingface. We shall start by understanding how to build very simple models (like Linear regression models for car price prediction, text classifiers for movie reviews, binary classifiers for malaria prediction) using Tensorflow and Huggingface transformers, to more advanced models (like object detection models with YOLO, lyrics generator model with GPT2 and Image generation with GANs)After going through this course and carrying out the different projects, you will develop the skill sets needed to develop modern deep-learning solutions that big tech companies encounter.You will learn:The Basics of Tensorflow (Tensors, Model building, training, and evaluation)Deep Learning algorithms like Convolutional neural networks and Vision TransformersEvaluation of Cla
Unlock the potential of Generative AI with our comprehensive course, "Mastering Generative AI: LLMs, Prompt Engineering & More." This course is designed for both beginners and seasoned developers looking to deepen their understanding of the rapidly evolving field of artificial intelligence.In this course, you will explore a wide range of essential topics, including:· Python Programming: Learn the fundamentals of Python, the go-to language for AI development, and become proficient in data manipulation using libraries like Pandas and NumPy.· Natural Language Processing (NLP): Dive into the world of NLP, mastering techniques for text processing, feature extraction, and leveraging powerful libraries like NLTK and SpaCy.· Deep Learning and Transformers: Understand the architecture of Transformer models, which are at the heart of many state-of-the-art AI applications. Discover the principles of deep learning and how to implement neural networks using TensorFlow and PyTorch.· Large Language Models (LLMs): Gain insights into LLMs, their training, and fine-tuning processes. Learn how to effectively use these models in various applications, from chatbots to content generation.· Retrieval-Augmented Generation (RAG): Explore the innovative concept of RAG, which combines retrieval techniques with generative models to enhance AI performance.· Prompt Engineering: Master the art of crafting effective prompts to improve the interaction with LLMs and optimize the output for specific tasks.· Vector Databases: Discover how to implement and utilize vector databases for storing and retrieving high-dimensional data, a crucial skill in managing AI-generated content.The course culminates in a Capstone Project, where you will apply everything you've learned to solve a real-world problem using Generative AI te
Hi there,Welcome to "Generative AI & ChatGPT Mastery for Data Science and Python" course.Master Generative AI, ChatGPT and Prompt Engineering for Data Science and Python from scratch with hands-on projectsArtificial Intelligence (AI) is transforming the way we interact with technology, and mastering AI tools has become essential for anyone looking to stay ahead in the digital age. In today's data-driven world, the ability to analyze data, draw meaningful insights, and apply machine learning algorithms is more crucial than ever. This course is designed to guide you through every step of that journey, from the basics of Exploratory Data Analysis (EDA) to mastering advanced machine learning algorithms, all while leveraging the power of ChatGPT-4o.Data science application is an in-demand skill in many industries worldwide — including finance, transportation, education, manufacturing, human resources, and banking. Explore data science courses with Python, statistics, machine learning, and more to grow your knowledge. Get data science training if you’re into research, statistics, and analytics.Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information about whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat.A machine learning course teach
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Learn How I'd learn ML in 2025 (if I could start over)
Interested in Machine Learning and Deep Learning ? Then this course is for you!This course is about the fundamental concepts of machine learning, deep learning, reinforcement learning and machine learning. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking.In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. We will use Python with SkLearn, Keras and TensorFlow.### MACHINE LEARNING ###Linear Regressionunderstanding linear regression modelcorrelation and covariance matrixlinear relationships between random variablesgradient descent and design matrix approachesLogistic Regressionunderstanding logistic regressionclassification algorithms basicsmaximum likelihood function and estimationK-Nearest Neighbors Classifierwhat is k-nearest neighbour classifier?non-parametric machine learning algorithmsNaive Bayes Algorithmwhat is the naive Bayes algorithm?classification based on probabilitycross-validation overfitting and underfittingSupport Vector Machines (SVMs)support vector machines (SVMs) and support vector classifiers (SVCs)maximum margin classifierkernel trickDecision Trees and Random Forestsdecision tree classifierrandom forest classifiercombining weak learnersBagging and
The Python for Data Science and Machine Learning course is designed to equip learners with a comprehensive understanding of Python programming, data science techniques, and machine learning algorithms. Whether you are a beginner looking to enter the field or a seasoned professional seeking to expand your skillset, this course provides the knowledge and practical experience necessary to excel in the rapidly growing field of data science.Course Objectives:1. Master Python Programming: Develop a strong foundation in Python programming, including syntax, data structures, control flow, and functions. Gain proficiency in using Python libraries such as NumPy, Pandas, and Matplotlib to manipulate and visualize data effectively.2. Data Cleaning and Preprocessing: Learn how to handle missing data, outliers, and inconsistent data formats. Acquire skills in data cleaning and preprocessing techniques to ensure the quality and reliability of datasets.3. Exploratory Data Analysis: Understand the principles and techniques of exploratory data analysis. Learn how to extract insights, discover patterns, and visualize data using statistical methods and Python libraries.4. Statistical Analysis: Gain a solid understanding of statistical concepts and techniques. Apply statistical methods to analyze data, test hypotheses, and draw meaningful conclusions.5. Machine Learning Fundamentals: Learn the foundations of machine learning, including supervised and unsupervised learning, regression, classification, and clustering. Understand the strengths and limitations of different machine learning algorithms.6. Machine Learning Implementation: Gain hands-on experience in implementing machine learning models using Python libraries such as scikit-learn. Learn how to train, evaluate, and optimize machine learning models.7. Feature Engineering and Selection: Develop skills in feature engineering to create mea
Section 1: Introduction to the CourseGet a complete overview of the course and what you will learn. Understand how ChatGPT-5 and AI tools can help you create, automate, and innovate efficiently.Section 2: Understanding AI EssentialsLearn the core concepts of Artificial Intelligence in a simple way. Understand how AI works, how it is trained, and how AI tools are transforming industries in 2025.Section 3: Prompt Engineering & Creating Effective PromptsDiscover the art of prompt engineering. Learn to communicate with AI models to get accurate, creative, and useful outputs. Explore prompt structures, chaining, and generating ideas and automation flows.Section 4: Creating AI PresentationsLearn to make AI-powered presentations quickly. Present your ideas clearly and professionally for college, work, or business with AI assistance.Section 5: AI Video GenerationStep into AI-driven video creation. Turn text prompts into professional-looking videos for marketing, education, or social media without needing any editing skills.Whether you are new to AI or already exploring tools like ChatGPT, this course will guide you step by step in a simple and practical way so you can apply AI in your studies, career, and daily life. join and start learning with our AI course start now
A comprehensive introduction to the mathematical principles that form the foundation of artificial intelligence and machine learning, bridging essential concepts with real-world AI applications.
If you are interested in Machine Learning, Neural Networks, Deep Learning, Deep Neural Networks (DNN), and Convolution Neural Networks (CNN) with an in-depth and clear understanding, then this course is for you.Topics are explained in detail. Concepts are developed progressively in a step by step manner. I sometimes spent more than 10 minutes discussing a single slide instead of rushing through it. This should help you to be in sync with the material presented and help you better understand it.The hands-on examples are selected primarily to make you familiar with some aspects of TensorFlow 2 or other skills that may be very useful if you need to run a large and complex neural network job of your own in the future.Hand-on examples are available for you to download.Please watch the first two videos to have a better understanding of the course.TOPICS COVEREDWhat is Machine Learning?Linear RegressionSteps to Calculate the ParametersLinear Regression-Gradient Descent using Mean Squared Error (MSE) Cost FunctionLogistic Regression: ClassificationDecision BoundarySigmoid FunctionNon-Linear Decision BoundaryLogistic Regression: Gradient DescentGradient Descent using Mean Squared Error Cost FunctionProblems with MSE Cost Function for Logistic RegressionIn Search for an Alternative Cost-FunctionEntropy and Cross-EntropyCross-Entropy: Cost Function for Logistic RegressionGradient Descent with Cross Entropy Cost FunctionLogistic Regression: Multiclass ClassificationIntroduction to Neural NetworkLogical OperatorsModeling Logical Operators using Perceptron(s)Logical Operators using Combination of Perceptron
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!This course is designed for ML practitioners who want to enhance their skills and move up the ladder with Deep Learning!This course is made to give you all the required knowledge at the beginning of your journey so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips, and tricks you would require to work in the Deep Learning space.It gives a detailed guide on Tensorflow and Keras along with in-depth knowledge of Deep Learning algorithms. All the algorithms are covered in detail so that the learner gains a good understanding of the concepts. One needs to have a clear understanding of what goes behind the scenes to convert a good model to a great model. This course will enable you to develop complex deep-learning architectures with ease and improve your model performance with several tips and tricks.Deep Learning Algorithms Covered:1. Feed Forward Networks (FFN)2. Convolutional Neural Networks (CNN)3. Recurring Neural Networks (RNN)4. Long Short-Term Memory Networks (LSTMs)5. Gated Recurrent Unit (GRU)6. Autoencoders7. Transfer Learning8. Generative Adversarial Networks (GANs)Our exotic journey will include the concepts of:1. The most important concepts of Tensorflow and Keras from very basic.2. The two ways of model building i.e. Sequential and Functional API.3. All the building blocks of Deep Learning models are explained in detail to enable students to make decisions while training their model and improving model performance.4. Hands-on learning of Deep Learning algorithms from the beginner
Learn AI Tools EXPLAINED: How to Use Them? (2025 Guide for Beginners)
A deep learning course that offers a comprehensive introduction to Autoencoders, Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs).
This beginner-level specialization consists of three courses that equip finance professionals with AI literacy and practical skills. It covers the fundamentals of AI, its applications in finance, risk management, and how to use generative AI for tasks like financial forecasting and risk assessments.
Welcome to the first Data Science and Machine Learning course with ChatGPT. Learn how to use ChatGPT to master complex Data Science and Machine Learning real-life projects in no time! Why is this a game-changing course?Real-world Data Science and Machine Learning projects require a solid background in advanced statistics and Data Analytics. And it would be best if you were a proficient Python Coder. Do you want to learn how to master complex Data Science projects without the need to study and master all the required basics (which takes dozens if not hundreds of hours)? Then this is the perfect course for you! What you can do at the end of the course:At the end of this course, you will know and understand all strategies and techniques to master complex Data Science and Machine Learning projects with the help of ChatGPT! And you don´t have to be a Data Science or Python Coding expert! Use ChatGPT as your assistant and let ChatGPT do the hard work for you! Use ChatGPT forthe theoretical part Python codingevaluating and interpreting coding and ML resultsThis course teaches prompting strategies and techniques and provides dozens of ChatGPT sample prompts toload, initially inspect, and understand unknown datasets clean and process raw datasets with Pandasmanipulate, aggregate, and visualize datasets with Pandas and matplotlibperform an extensive Explanatory Data Analysis (EDA) for complex datasetsuse advanced statistics, multiple regression analysis, and hypothesis testing to gain further insightsselect the most suitable Machine Learning Model for your prediction tasks (Model Selection)evaluate and interpret the performance of your Machine Learning models (Perfo
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This specialization covers key AI concepts for cybersecurity including anomaly detection, generative AI, fraud detection, and intrusion prevention. Learners will gain skills in threat detection, cyber threat intelligence, and network analysis. The course is designed for beginners and can be completed in 3-6 months.
This course explores the application of AI techniques to enhance engineering design and optimization. It covers generative design, evolutionary algorithms, and topology optimization, providing a comprehensive introduction to using AI for design creativity and process streamlining.
Learn Streamlit for LLM Applications
AI is omnipresent in our modern world. It is in your phone, in your laptop, in your car, in your fridge and other devices you would not dare to think of. After thousands of years of evolution, humanity has managed to create machines that can conduct specific intelligent tasks when trained properly. How? Through a process called machine learning or deep learning, by mimicking the behaviour of biological neurons through electronics and computer science. Even more than it is our present, it is our future, the key to unlocking exponential technological development and leading our societies through wonderful advancements. As amazing as it sounds, it is not off limits to you, to the contrary!We are both engineers, currently designing and marketing advanced ultra light electric vehicles. Albert is a Mechanical engineer specializing in advanced robotics and Eliott is an Aerospace Engineer specializing in advanced space systems with past projects completed in partnership with the European Space Agency. The aim of this course is to teach you how to fully, and intuitively understand neural networks, from their very fundamentals. We will start from their biological inspiration through their mathematics to go all the way to creating, training and testing your own neural network on the famous MNIST database.It is important to note that this course aims at giving you a complete and rich understanding of neural networks and AI, in order to give you the tools to create your own neural networks, whatever the project or application. We do this by taking you through the theory to then apply it on a very hands-on MATLAB project, the goal being for you to beat our own neural network's performance!This course will give you the opportunity to understand, use and create:How to emulate real brains with neural networks.How to represent and annotate neural networks.How to build and compute neural ne
This course will help you in understanding of the Linear Algebra and math’s behind Data Science and Machine Learning. Linear Algebra is the fundamental part of Data Science and Machine Learning. This course consists of lessons on each topic of Linear Algebra + the code or implementation of the Linear Algebra concepts or topics.There’re tons of topics in this course. To begin the course:We have a discussion on what is Linear Algebra and Why we need Linear AlgebraThen we move on to Getting Started with R, where you will learn all about how to setup the R environment, so that it’s easy for you to have a hands-on experience.Then we get to the essence of this course;Vectors & Operations on VectorsMatrices & Operations on MatricesDeterminant and InverseSolving Systems of Linear EquationsNorms & Basis VectorsLinear IndependenceMatrix FactorizationOrthogonalityEigenvalues and EigenvectorsSingular Value Decomposition (SVD)Again, in each of these sections you will find R code demos and solved problems apart from the theoretical concepts of Linear Algebra.You will also learn how to use the R's pracma, matrixcalc library which contains numerous functions for matrix computations and solving Linear Algebric problems. So, let’s get started….
Preparing for a deep learning certification can feel overwhelming, especially with the wide range of neural network concepts, frameworks, and exam-style questions you need to master. This exam prep course is designed to help you build confidence, sharpen your knowledge, and get exam-ready with structured practice.Unlike generic tutorials, this course is focused on exam preparation. You’ll review the essential foundations of neural networks, dive into advanced architectures, and practice applying your skills across major frameworks such as TensorFlow and PyTorch. Each module is carefully aligned with the topics most commonly assessed in certification exams.By the end of this course, you will not only reinforce your theoretical understanding but also practice solving question styles that mirror real exam challenges. While this is not an official certification product, it provides the structure, depth, and practice environment you need to approach the test with clarity.What you’ll gain from this course:Comprehensive coverage of key deep learning concepts and frameworksPractice-based learning through 134 exam-style questions across 4 modulesClarity on architectures such as CNNs, RNNs, LSTMs, and TransformersHands-on readiness with TensorFlow and PyTorch fundamentalsAwareness of exam strategies to manage time, avoid common pitfalls, and improve accuracyWho is this course for?Learners preparing for deep learning certification examsProfessionals aiming to validate their AI/ML knowledgeStudents who want structured revision in neural networks and frameworksImportant Note: This is not an official certification course and is not affiliated with any certifyin
This course introduces you to working with time series data, starting from basics like trends and seasonality and moving to advanced forecasting techniques. It covers both classical statistical models and modern machine learning approaches, including deep learning architectures like RNNs and LSTMs.
Unlock the boundless potential of data by enrolling in our comprehensive course, "Mastering Machine Learning, Data Science, Neural Networks, and Artificial Intelligence with Python and Libraries." This meticulously crafted program is designed to empower individuals with the skills and knowledge needed to navigate the dynamic landscape of modern technology.Course Overview:In this immersive learning journey, participants will delve into the core principles of Machine Learning, Data Science, Neural Networks, and Artificial Intelligence using Python as the primary programming language. The course is structured to cater to both beginners and intermediate learners, ensuring a gradual progression from fundamental concepts to advanced applications.Key Highlights:Foundations of Machine Learning:Gain a solid understanding of machine learning fundamentals, algorithms, and models.Explore supervised and unsupervised learning techniques.Master feature engineering, model evaluation, and hyperparameter tuning.Data Science Essentials:Learn the art of extracting valuable insights from data.Acquire proficiency in data manipulation, cleaning, and exploratory data analysis.Harness the power of statistical analysis for informed decision-making.Neural Networks and Deep Learning:Dive into the realm of neural networks and deep learning architectures.Understand the mechanics of artificial neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).Implement state-of-the-art deep learning models using Python libraries.Artificial Intelligence (AI) Applications:Explore the practical applications of AI in various industries.Wor
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In today's data-driven world, the ability to harness and interpret data is not just a valuable skill but a crucial advantage. Whether you're an aspiring data scientist, a seasoned professional looking to expand your skill set, or an entrepreneur aiming to leverage data for strategic decisions, our comprehensive course on data science offers a transformative learning experience.Course OverviewOur course begins with a foundational exploration of data science, introducing you to its principles and importance in various industries. You'll delve into the distinctions between data science, data engineering, and data analysis, gaining a clear understanding of their respective roles and applications. Through real-world case studies and examples, you'll discover how data science drives innovation and impacts decision-making processes across different sectors.Essential Tools and TechnologiesTo equip you with the tools needed for effective data analysis, the course covers essential programming languages such as Python . Whether you're manipulating data with Pandas, performing numerical operations with NumPy, or creating insightful visualizations with Matplotlib and Seaborn, you'll develop a versatile skill set that forms the backbone of data science projects.Practical Skills DevelopmentA significant focus of the course is hands-on learning. You'll gain practical experience in gathering, cleaning, and analyzing data from diverse sources. Through interactive exercises and projects, you'll hone your ability to transform raw data into actionable insights that drive business decisions.Environment Setup and Best PracticesNavigating the data science environment can be daunting, especially for beginners. That's why we guide you through the setup of Python and Jupyter Notebook on both Windows and macOS, ensuring you're equipped with the right tools from the start. You'll learn to create and manage virtual environments, enhancing your ability to work efficiently a
Welcome to the Python for Data Science Bootcamp: From Zero to Hero. In this course, we're going to learn how to use Python for Data Science. In this practical course, we'll learn how to collect data, clean data, make visualizations and build a machine learning model using Python.The main goal of this course is to take your programming and analytical skills to the next level to build your career in Data Science. To achieve this goal, we're going to solve hundreds of exercises and many cool projects that will help you put into practice all the programming concepts used in Data Science.We'll learn the top Python Libraries used in Data Science such as Pandas, Numpy and Scikit Learn and we will use them to learn to solve tasks data scientists deal with on a daily basis (Data Cleaning, Data Visualization, Data Collection and Model Building)This course covers 4 main sections.1. Python for Data Science Crash Course: In the first section, we'll learn all the Python core concepts you need to know for Data Science. We'll learn how to use variables, lists, dictionaries and more.2. Python for Data Analysis: We'll learn Python libraries used for data analysis such as Pandas and Numpy. Both are great tools for exploring and working with data. We'll use Pandas and Numpy to deal with data science tasks such as cleaning and preparing data.3. Python for Data Visualization: In the third section, we'll learn how to make static and interactive visualizations with Pandas. Also, I'll show you some techniques to properly make data visualization.4. Machine Learning with Python: In the fourth section, we'll learn scikit-learn by solving a text classification problem in Python. This is the most popular machine learning library in Python and we'll not only learn how to implement machine learning algorithms in Python but also we'll learn the core concepts behind the most common algorithms using practical examples.Bonus (Basic Web Scraping with Python): Remember that at the end o
An in-depth introduction to artificial intelligence (AI), covering its fundamental concepts, historical development, and practical applications across various industries, with a focus on public sector applications.
Learn CS50s Introduction to Programming with Python
This course covers a comprehensive curriculum from the basics of Python and math to advanced topics in Machine Learning, Deep Learning, and LLMs, including Transformers. It appears to be a broad AI course that likely covers the hardware aspects of training and deploying models.
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Learn Artificial Intelligence Foundations: Machine Learning
4.13/5.0 rating. 100% say "valuable information." 100% say "clear explanations." 100% say "knowledgeable instructor." Beginner-friendly introduction to artificial intelligence fundamentals, machine learning, and real-world AI applications.Master Artificial Intelligence basics including machine learning concepts, neural networks, deep learning principles, and AI applications across industries. Learn AI fundamentals without coding—designed for engineers, business professionals, and beginners exploring how intelligent systems transform healthcare, finance, manufacturing, energy, and project management.WHAT YOU'LL LEARNAI Fundamentals & TypesUnderstand what artificial intelligence is, differentiate between narrow AI, general AI, and super AI, and learn how AI systems learn from data. Explore supervised learning, unsupervised learning, and reinforcement learning concepts without complex mathematics.Machine Learning BasicsMaster foundational machine learning concepts including training data, algorithms, model accuracy, and prediction systems. Learn how ML powers recommendation engines, fraud detection, and predictive maintenance without writing code.Neural Networks & Deep LearningUnderstand how artificial neural networks mimic human brain structure, learn about layers, nodes, activation functions, and how deep learning enables image recognition, natural language processing, and autonomous systems.Data in AI SystemsLearn why data is critical for AI, understand training datasets, data quality requirements, data preprocessing, and how bias in data creates biased AI models. Explore data ethics and responsible AI practices.AI Applications Across IndustriesDiscover real-world AI uses in energy systems (predictive maintenance, grid optimization), manufacturing (quality control, robotics), healthcare (diagnostics, drug disco
This course from Columbia University provides an introduction to causal inference. You will learn about the key concepts in causal inference, such as confounding, selection bias, and instrumental variables. The course includes lectures, quizzes, and a final project.
This course provides a comprehensive introduction to computer vision, covering topics like image processing, object detection, and the application of deep learning models in vision systems.
Learn Google’s AI Course for Beginners (in 10 minutes)!
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This course from the University of Pennsylvania provides a comprehensive introduction to causal inference, covering topics like potential outcomes, confounding, directed acyclic graphs (DAGs), matching, and instrumental variables.
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A 12-week, 24-lesson curriculum all about Artificial Intelligence for beginners from Microsoft.
Learn Getting Started with Stable Diffusion in 2024 for Absolute Beginners
Learn AI Art For Beginners - Stable Diffusion Crash Course Syllabus Overview
A comprehensive course covering a wide range of topics from the basics of RAG to advanced techniques like fine-tuning and RAG agents. It includes building a basic RAG pipeline, advanced retrieval methods, and optimizing for production.
Learn Transformers for beginners | What are they and how do they work
A free, self-paced bootcamp that teaches how to build professional web applications using Bubble. It covers beginner to intermediate skills with a focus on best practices developed by the Airdev agency.
Watch AI learn to play games and solve problems. Fun, visual approach to understanding AI and machine learning concepts.
Course DescriptionStay ahead in the world of AI - ML with this completely updated course covering . Machine Learning. Deep Learning. Large Language Models (LLMs). Retrieval-Augmented Generation (RAG). AI Agents. Explainable AI (XAI). AutoML using Google Vertex AI This is a hands-on course, designed for active learning. You are encouraged to practice along with the trainer during sessions or immediately after each lecture to build real, practical skills.The content is organized into 21 manageable days, allowing you to learn systematically without feeling overwhelmed. Whether you're a beginner or an experienced professional, you can start from the basics or jump straight to the advanced sections that interest you most.The course is taught by an industry veteran and founder of an AI startup, bringing real-world insights and project-based learning to every module.Running successfully for the past three years, the course has been regularly refreshed to reflect the latest advancements — including cutting-edge topics like Explainable AI, AutoML on Google Vertex, RAG pipelines, and AI Agent frameworks.If you’re looking for a complete, modern, and industry-focused AI learning experience — this is your perfect starting point.Enroll today and build the AI expertise the future demands!What you’ll learn:Build a strong foundation in Machine Learning and Deep Learning conceptsUnderstand and fine-tune Large Language Models (LLMs) for various applicationsDesign and implement Retrieval-Augmented Generation (RAG)</st
Welcome to the Deep Learning Fundamentals course on Udemy! Are you ready to unlock the power of neural networks and delve into the exciting world of artificial intelligence? Look no further! This comprehensive course is designed to equip you with the essential knowledge and practical skills needed to become proficient in both Tensorflow and Pytorch based deep learning together!Deep learning has revolutionized the field of AI, enabling machines to learn from vast amounts of data and make accurate predictions, recognize patterns, and perform complex tasks. In this course, we will demystify the concepts behind deep learning and guide you through hands-on exercises to build and train your neural networks.Here's an overview of what you'll learn:Introduction to Deep Learning:Understand the fundamentals of artificial neural networks.Explore the history and evolution of deep learning.Gain insights into real-world applications and their impact.Neural Networks and Architectures:Study the structure and functioning of artificial neurons.Learn about various neural network architectures, including feedforward, convolutional, and recurrent networks.Explore activation functions, weight initialization, and regularization techniques.Building Deep Learning Models:Implement deep learning models using popular frameworks such as TensorFlow or PyTorch.Understand the process of data preprocessing, including feature scaling and one-hot encoding.Design effective training and validation sets for model evaluation.Training Neural Networks:Grasp the concept of backpropagation and how it enables model training.Explore optimization algorithms like stochastic gradient descent (SGD) and Adam.Learn techniques to prevent overfitting, such as dropout and ea
Welcome to the "Deep Learning Neural Networks with TensorFlow" course! This comprehensive program is designed to equip you with the essential knowledge and hands-on skills required to navigate the exciting field of deep learning using TensorFlow.Overview: In this course, you will embark on a journey through the fundamentals and advanced concepts of deep learning neural networks. We'll start by providing you with a solid foundation, introducing the core principles of neural networks, including the scenario of Perceptron and the creation of neural networks using TensorFlow.Hands-on Projects: To enhance your learning experience, we have incorporated practical projects that allow you to apply your theoretical knowledge to real-world scenarios. The "Face Mask Detection Application" project in Section 2 and the "Implementing Linear Model with Python" project in Section 3 will provide you with valuable hands-on experience, reinforcing your understanding of TensorFlow.Advanced Applications: Our course goes beyond the basics, delving into advanced applications of deep learning. Section 4 explores the fascinating realm of automatic image captioning for social media using TensorFlow. You will learn to preprocess data, define complex models, and deploy applications, gaining practical insights into the cutting-edge capabilities of deep learning.Why TensorFlow? TensorFlow is a leading open-source deep learning framework, widely adopted for its flexibility, scalability, and extensive community support. Whether you're a beginner or an experienced professional, this course caters to learners of all levels, guiding you through the intricacies of deep learning with TensorFlow.Get ready to unravel the mysteries of neural networks, develop practical skills, and unleash the power of TensorFlow in the dynamic field of deep learning. Join us on this exciting learning journey, and let's dive deep into the
Notice: Effective Dec 24, 2024, this course has been thoroughly updated. Rest assured, it will consistently be refreshed to ensure its ongoing relevance and effectiveness.Unlock the Future of AI: Master Generative AI & NLP with PythonEmbark on a Revolutionary Journey into the World of AI: Become a Master of Generative AI & Natural Language Processing (NLP)Are you ready to unlock the full potential of Artificial Intelligence? This comprehensive, two-part course is designed to take you on an in-depth, hands-on journey through the cutting-edge fields of Generative AI and Natural Language Processing (NLP). Whether you're a beginner looking to enter the world of AI, a professional seeking to upgrade your skills, or an innovator aiming to stay ahead of the curve, this course offers everything you need to master these transformative technologies and prepare for the future.Course Overview:Our Generative AI & NLP with Python course will empower you to dive deep into the heart of AI technologies. Through expert-led instruction and hands-on practice, you will acquire the skills necessary to develop, implement, and leverage AI tools and techniques in real-world applications. This course goes beyond theoretical knowledge, focusing on practical, real-world projects to ensure that you not only learn but also apply what you’ve learned.Part 1: Generative AI Unleashed - Transforming Ideas into RealityIn the first part of this course, we will explore the exciting world of Generative AI and Large Language Models (LLMs). You’ll discover how these technologies are revolutionizing industries across the globe by generating creative, data-driven solutions to complex problems.Introduction to Generative AI: Learn the essential principles, history, and evolution of Gener
Welcome to the course "Modern Computer Vision & Deep Learning with Python & PyTorch"! Imagine being able to teach computers to see just like humans. Computer Vision is a type of artificial intelligence (AI) that enables computers and machines to see the visual world, just like the way humans see and understand their environment. Artificial intelligence (AI) enables computers to think, where Computer Vision enables computers to see, observe and interpret. This course is particularly designed to provide a comprehensive, hands-on experience in applying Deep Learning techniques to major Computer Vision problems including Image Classification, Semantic Segmentation, Instance Segmentation, and Object Detection. In this course, you'll start with an introduction to the basics of Computer Vision and Deep Learning, and learn how to implement, train, test, evaluate and deploy your own models using Python and PyTorch for Image Classification, Image Segmentation, and Object Detection. Computer Vision plays a vital role in the development of autonomous vehicles. It enables the vehicle to perceive and understand its surroundings to detect and classify various objects in the environment, such as pedestrians, vehicles, traffic signs, and obstacles. This helps to make informed decisions for safe and efficient vehicle navigation. Computer Vision is used for Surveillance and Security using drones to track suspicious activities, intruders, and objects of interest. It enables real-time monitoring and threat detection in public spaces, airports, banks, and other security-sensitive areas. Today Computer Vision applications in our daily life are very common including Face Detection in cameras and cell phones, logging in to devices with fingerprints and face recognition, interactive games, MRI, CT scans, image guided surgery and much more. This comprehensive course is especially designed to give you hands-on experience using Python and Pytorch coding to build, train, test and deploy
Part of the IBM Data Analyst Professional Certificate, this course covers the fundamentals of data analysis using Python, including working with data, exploratory data analysis, and an introduction to machine learning models.
Generative AI: Prompt Engineering with ChatGPT and MidJourney is a course designed to teach students the fundamentals of prompt engineering and how to use generative AI models like ChatGPT to generate text.The course will cover topics such as the basics of generative AI, natural language processing, and prompt engineering. Students will learn how to use the MidJourney platform to build and train their own ChatGPT models.Throughout the course, students will work on hands-on projects that will help them develop their skills in prompt engineering and generative AI. They will also learn about the ethical considerations of using AI models for text generation and how to mitigate potential risks.By the end of the course, students will have a solid understanding of how to use generative AI models to generate text and will be equipped with the skills needed to create their own AI-powered language models.The course will be taught by experts in the field of AI and natural language processing. They will provide lectures and live demonstrations to guide students through the process of building and training their own generative AI models.Students will also have the opportunity to work on group projects and receive feedback from their peers and instructors. This collaborative learning environment will allow students to learn from each other and share their experiences.One of the key aspects of the course will be the focus on prompt engineering. Students will learn how to construct effective prompts that will guide their AI models to generate coherent and relevant text. They will also learn about different types of prompts and how to customize them for specific tasks.Throughout the course, students will use the MidJourney platform to build and train their own ChatGPT models. MidJourney is a user-friendly platform that provides all the tools necessary for building and deploying AI models. Students will have access to a pre-built dataset and will be able to experiment with differ
Unlock the power of Generative AI and learn how to build real-world applications using cutting-edge tools like ChatGPT, LangChain, Hugging Face, and more — even if you’re not a developer.This course starts with a fast-track module for non-coders, introducing you to practical no-code AI tools like Zapier, Canva AI, and Notion AI. You’ll quickly understand how Generative AI works — no math, no jargon, just clear and practical insights.You’ll then dive deep into Large Language Models (LLMs), learning how models like GPT and open-source alternatives function, and how to interact with them through effective prompt engineering. Understand the difference between OpenAI's APIs, local models, and when to use each.The course progresses with hands-on projects using the OpenAI API and LangChain to build intelligent assistants, custom chatbots, and agent-based tools. You’ll explore how to integrate tools and functions, use LangGraph for complex multi-step workflows, and build applications like weather and calculator agents.You'll also learn how to incorporate Hugging Face models, perform text classification, and explore LoRA fine-tuning basics — all with step-by-step guidance. The Retrieval-Augmented Generation (RAG) section will teach you how to connect AI with custom documents, PDFs, and websites using embeddings and vector databases like Pinecone, ChromaDB, and FAISS.We’ll also cover critical topics like AI safety, bias, responsible prompt engineering, and deploying your apps using tools like Streamlit, Gradio, and Hugging Face Spaces. You’ll even learn how to add a simple frontend with HTML/CSS/JS to showcase your work live.By the end of the course, you’ll complete real-world capstone projects such as a Social Media Post Generator and a Podcast AI Summarizer, and learn how to build a portfolio on GitHub that demonstrates your skills to potential clients or employers.Whether you're a developer, freelancer, entrepreneur, or aspiring AI bui
A free online course with accompanying textbook and video lectures that provides a comprehensive introduction to causal inference from a machine learning perspective.
Preparado para um mergulho eletrizante no universo da tecnologia de linguagem? Prepare-se para entrar de cabeça no mundo maneiro do LangChain com "LangChain 101 para Iniciantes (OpenAI / ChatGPT / LLMOps)". Aqui, você vai aprender a mandar bem usando o LangChain e Modelos de Linguagem de Grande Porte (LLMs) para criar suas próprias aplicações em Python.O objetivo deste curso é moleza - te dar todas as ferramentas para começar sua aventura no LangChain. Vamos te mostrar como usar diferentes LLMs dos gigantes do mercado, OpenAI e HuggingFace, e desvendar o truque de chamar prompts, criar templates e ligar tudo isso para montar um sistema interativo e firmeza.Mas calma que tem mais! Vamos nos aprofundar no mundo dos chatbots e descobrir como a memória funciona no LangChain. Pra fechar com chave de ouro, um tutorial detalhado sobre como aplicar esses LLMs poderosos nos seus próprios documentos.Este curso não é só informativo — é diversão garantida. Usamos memes, exemplos do dia a dia e uma pegada descontraída para fazer desta jornada no mundo do LangChain algo super prazeroso.Dá um chega pra lá naqueles cursos chatos e longos que são puro blá-blá-blá. Este aqui é direto ao ponto, perfeito para desenvolvedores Python que querem um atalho para o mundo do LangChain e LLMs. Sabemos que seu tempo é ouro, então condensamos tudo que é essencial em uma horinha top."LangChain 101 para Iniciantes" é o seu passaporte para entender e botar a mão na massa com o LangChain. Ao final deste curso, você não só vai entender tudinho sobre LangChain, mas também estará pronto para encarar seu próximo projeto com um arsenal novinho em folha.Não fica aí parado — bora criar o futuro juntos. Mergulhe nesse mundo incrível do LangChain e Modelos de Linguagem de Grande Porte e se divirta muito pelo caminho!
Course ContentsDeep Learning and revolutionized Artificial Intelligence and data science. Deep Learning teaches computers to process data in a way that is inspired by the human brain.This is complete and comprehensive course on deep learning. This course covers the theory and intuition behind deep learning models and then implementing all the deep learning models both in Pytorch and Tensor flow.Practical Oriented explanations Deep Learning Models with implementation both in Pytorch and Tensor Flow.No need of any prerequisites. I will teach you everything from scratch.Job Oriented StructureSections of the Course· Introduction of the Course· Introduction to Google Colab· Python Crash Course· Data Preprocessing· Regression Analysis· Logistic Regression· Introduction to Neural Networks and Deep Learning· Activation Functions· Loss Functions· Back Propagation· Neural Networks for Regression Analysis· Neural Networks for Classification· Dropout Regularization and Batch Normalization· Optimizers· Adding Custom Loss Function and Custom Layers to Neural Networks· Convolutional Neural Network (CNN)· One Dimensional CNN· Setting Early Stopping Criterion in CNN· Recurrent Neural Network (RNN)· Long Short-Term Memory (LSTM) Network· Bidirectional LSTM· Generative Adversarial Network (GAN)· DCGANs· Autoencoders· LSTM Autoencoders· Variational Autoencoders· Neural Style Transfer· Transformers· Vision Transformer· Time Series Transformers. K-means Clustering. Principle Component Analysis. Deep Learning Models with implementation both in Pytorch and Tensor Flow.
Unlock the potential of Generative AI with our comprehensive course, "Gen AI Masters 2025 - From Python To LLMs and Deployment" This course is designed for both beginners and seasoned developers looking to deepen their understanding of the rapidly evolving field of artificial intelligence.Learn how to build Generative AI applications using Python and LLMs. Understand prompt engineering, explore vector databases like FAISS, and deploy real-world AI chatbots using RAG architecture.In this course, you will explore a wide range of essential topics, including:Python Programming: Learn the fundamentals of Python, the go-to language for AI development, and become proficient in data manipulation using libraries like Pandas and NumPy.Natural Language Processing (NLP): Dive into the world of NLP, mastering techniques for text processing, feature extraction, and leveraging powerful libraries like NLTK and SpaCy.Deep Learning and Transformers: Understand the architecture of Transformer models, which are at the heart of many state-of-the-art AI applications. Discover the principles of deep learning and how to implement neural networks using TensorFlow and PyTorch.Large Language Models (LLMs): Gain insights into LLMs, their training, fine-tuning processes (including PEFT, LoRA, and QLoRA), and learn how to effectively use these models in various applications, from chatbots to content generation.Retrieval-Augmented Generation (RAGs): Explore the innovative concept of RAG, which combines retrieval techniques with generative models to enhance AI performance. You'll also learn about RAG evaluation methods, including the RAGAS framework, BLEU, ROUGE, BARScore, and BERTScore.Prompt Engineering</str
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Welcome to the gateway to your journey into Python for Machine Learning & Deep Learning!Unlock the power of Python and delve into the realms of Machine Learning and Deep Learning with our comprehensive course. Whether you're a beginner eager to step into the world of artificial intelligence or a seasoned professional looking to enhance your skills, this course is designed to cater to all levels of expertise.What sets this course apart?Comprehensive Curriculum: Our meticulously crafted curriculum covers all the essential concepts of Python programming, machine learning algorithms, and deep learning architectures. From the basics to advanced techniques, we've got you covered.Hands-On Projects: Theory is important, but practical experience is paramount. Dive into real-world projects that challenge you to apply what you've learned and reinforce your understanding.Expert Guidance: Learn from industry expert who has years of experience in the field. Benefit from his insights, tips, and best practices to accelerate your learning journey.Interactive Learning: Engage in interactive lessons, quizzes, and exercises designed to keep you motivated and actively involved throughout the course.Flexibility: Life is busy, and we understand that. Our course offers flexible scheduling options, allowing you to learn at your own pace and convenience.Career Opportunities: Machine Learning and Deep Learning are in high demand across various industries. By mastering these skills, you'll open doors to exciting career opportunities and potentially higher earning potential.Are you ready to embark on an exhilarating journey into the world of Python for Machine Learning & Deep Learning? Enroll now and take the first step towards becoming a proficient AI practitioner!
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Unlock the Power of Calculus in Machine Learning, Deep Learning, Data Science, and AI with Python: A Comprehensive Guide to Mastering Essential Mathematical Skills"Are you striving to elevate your status as a proficient data scientist? Do you seek a distinctive edge in a competitive landscape? If you're keen on enhancing your expertise in Machine Learning and Deep Learning by proficiently applying mathematical skills, this course is tailor-made for you.Calculus for Deep Learning: Mastering Calculus for Machine Learning, Deep Learning, Data Science, Data Analysis, and AI using PythonEmbark on a transformative learning journey that commences with the fundamentals, guiding you through the intricacies of functions and their applications in data fitting. Gain a comprehensive understanding of the core principles underpinning Machine Learning, Deep Learning, Artificial Intelligence, and Data Science applications.Upon mastering the concepts presented in this course, you'll gain invaluable intuition that demystifies the inner workings of algorithms. Whether you're crafting self-driving cars, developing recommendation engines for platforms like Netflix, or fitting practice data to a function, the essence remains the same.Key Learning Objectives:Function Fundamentals: Initiate your learning journey by grasping the fundamental definitions of functions, establishing a solid foundation for subsequent topics.Data Fitting Techniques: Progress through the course, delving into data fitting techniques essential for Machine Learning, Deep Learning, Artificial Intelligence, and Data Science applications.Approximation Concepts: Explore important concepts related to approximation, a cornerstone for developing robust models in Machine Learning, Deep Learning, Artificial Intelligence, and Data Science.Neural Network Training: Leverage you
This University of London course provides a practical introduction to the K-Means clustering algorithm, with a focus on the underlying statistical concepts.
Learn how to build AI agents and LLM-powered applications that automate tasks and connect tools with ease.In this course, you'll begin with the basics of workflow automation and move step by step into building your own AI-powered agents. You’ll learn how to connect APIs, integrate powerful tools like OpenAI, Google Gemini, Claude, and build LLM applications using LangChain and other frameworks.This course is designed for both beginners and those with some experience. Whether you're looking to automate daily tasks, explore the potential of generative AI, or build scalable LLM apps, you’ll gain the skills to create powerful AI agents—without needing advanced coding knowledge.What You’ll Learn in This Course:Section 1: IntroductionWhat are AI Agents?What is n8n?What is n8n and What are the Benefits of Using It?Workflows, Automation, AI Agents - What’s the Difference?n8n Pricing Explained: Which Plan Is Right for You?How to Sign in to n8n: Step-by-Step GuideExploring the n8n User InterfaceSection 2: Set up Google Credentials in n8nHow to Set up Google Credentials in n8nSection 3: Lead Workflow: Auto-Save to Google Sheets, Filter, and Send Emails by BudgetCreate a trigger in n8nCreate an action in n8nWriting Custom Functions in n8nUsing Filters in n8nUsing the Switch Function in n8nUsing the Merge Node in n8nSection 4: Creating an AI Agent to Manage Calendar and Task Automation in n8nUsing an AI Agent to Create Events in Google CalendarUsing an AI Agent to Get Event Details from Google Calendar</l
Artificial Intelligence is transforming the world — and AI agents are at the heart of that revolution. From virtual assistants and research bots to automated problem-solvers, agents are the next leap beyond simple chatbots. In this hands-on course, you’ll learn step-by-step how to build your own working AI agent using LangChain, Python, and the OpenAI API — no advanced technical experience required.We’ll start from the very beginning. You’ll understand what AI really means, how agents differ from chatbots, and where they’re being used in the real world. Then, you’ll dive into the practical side — setting up your Python environment, installing key tools, and connecting APIs like OpenAI and SerpAPI to bring intelligence and search power to your creations.Once your setup is complete, we’ll explore the core concepts of LangChain — including chains, agents, prompts, and tools. You’ll learn how to create prompt templates, build your own tools, and finally, connect them all to create an AI agent that can think, search, and respond intelligently. By the end, you’ll not only understand how LangChain works, but also how to build, test, and customize your own AI-powered assistants for real-world use.Whether you’re an aspiring developer, a curious beginner, or an entrepreneur exploring the future of automation, this course gives you the practical foundation you need to start building with confidence.By completing this course, you’ll be able to:Understand the fundamentals of AI and LangChain Set up your development environment from scratchBuild and test working AI agents using Python and OpenAIExtend your agents with real-world tools and APIsJoin today, and take your first step toward building intelligent AI agents that can reason, search, and act — all powered by LangChain.
Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Tensorflow is Google’s popular offering for machine learning and deep learning. It has become a popular choice of tool for performing fast, efficient, and accurate deep learning. TensorFlow is one of the most comprehensive libraries for implementing deep learning.This comprehensive 2-in-1 course is your step-by-step guide to exploring the possibilities in the field of deep learning, making use of Google's TensorFlow. You will learn about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data with the help of insightful examples that you can relate to and show how these can be exploited in the real world with complex raw data. You will also learn how to scale and deploy your deep learning models on the cloud using tools and frameworks such as asTensorFlow, Keras, and Google Cloud MLE. This learning path presents the implementation of practical, real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient deep learning.This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Hands-on Deep Learning with TensorFlow, is designed to help you overcome various data science problems by using efficient deep learning models built in TensorFlow. You will begin with a quick introduction to TensorFlow essentials. You will then learn deep neural networks for different problems and explore the applications of convolutional neural networks on two real datasets. You will also learn how autoencoders can be used for efficient data representation. Finally, you will understand some of the important techniques to implement generative adversarial networks.The second course,
This course is designed to provide a comprehensive, hands-on experience in applying Deep Learning techniques to Semantic Image Segmentation problems. Are you ready to take your understanding of deep learning to the next level and learn how to apply it to real-world problems? In this course, you'll learn how to use the power of Deep Learning to segment images and extract meaning from visual data. You'll start with an introduction to the basics of Semantic Segmentation using Deep Learning, then move on to implementing and training your own models for Semantic Segmentation with Python and PyTorch.This course is designed for a wide range of students and professionals, including but not limited to:Machine Learning Engineers, Deep Learning Engineers, and Data Scientists who want to apply Deep Learning to Image Segmentation tasks Computer Vision Engineers and Researchers who want to learn how to use PyTorch to build and train Deep Learning models for Semantic SegmentationDevelopers who want to incorporate Semantic Segmentation capabilities into their projectsGraduates and Researchers in Computer Science, Electrical Engineering, and other related fields who want to learn about the latest advances in Deep Learning for Semantic SegmentationIn general, the course is for Anyone who wants to learn how to use Deep Learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of Semantic Segmentation using Python and PyTorchThe course covers the complete pipeline with hands-on experience of Semantic Segmentation using Deep Learning with Python and PyTorch as follows:Semantic Image Segmentation and its Real-World Applications in Self Driving Cars or Autonomous Vehicles etc.De
Obtain skills in one of the most sort after fields of this centuryIn this course, you'll learn how to get started in data science. You don't need any prior knowledge in programming. We'll teach you the Python basics you need to get started. Here are some of the items we will cover in this courseThe Data Science ProcessPython for Data ScienceNumPy for Numerical ComputationPandas for Data ManipulationMatplotlib for VisualizationSeaborn for Beautiful VisualsPlotly for Interactive VisualsIntroduction to Machine LearningDask for Big DataPower BI DesktopGoogle Data StudioAssociation Rule Mining - AprioriDeep Learning Apache Spark for Handling Big DataFor the machine learning section here are some items we'll cover :How Algorithms WorkAdvantages & Disadvantages of Various AlgorithmsFeature ImportancesMetricsCross-ValidationFighting OverfittingHyperparameter TuningHandling Imbalanced DataTensorFlow & KerasAutomated Machine Learning(AutoML)Natural Language ProcessingThe course also contains exercises and solutions that will help you practice what you have learned. By enrolling in this course, you'll have lifetime access to the videos and Notebooks. Purchasing the course also comes with a 30-day money-back guarantee, so you can try it at no risk at all. Let's now add Data Science, Machine Learning, and Deep Learning to your CV. See you inside the course. The course also contains exercises and solutions that will help you practice what you have learned. By enrolling in this course
Are you interested in unlocking the full potential of Artificial Intelligence? Do you want to learn how to create powerful image recognition systems that can identify objects with incredible accuracy? If so, then our course on Deep Learning with Python for Image Classification is just what you need! In this course, you will learn Deep Learning with Python and PyTorch for Image Classification using Pre-trained Models and Transfer Learning. Image Classification is a computer vision task to recognize an input image and predict a single-label or multi-label for the image as output using Machine Learning techniques. Embark on a journey into the fascinating world of deep learning with Python and PyTorch, tailored specifically for image classification tasks. In this hands-on course, you'll delve deep into the principles and practices of deep learning, mastering the art of building powerful neural networks to classify images with remarkable accuracy. From understanding the fundamentals of convolutional neural networks to implementing advanced techniques using PyTorch, this course will equip you with the knowledge and skills needed to excel in image classification projects.Deep learning has emerged as a game-changer in the field of computer vision, revolutionizing image classification tasks across various domains. Understanding how to leverage deep learning frameworks like PyTorch to classify images is crucial for professionals and enthusiasts alike. Whether you're a data scientist, software engineer, researcher, or student, proficiency in deep learning for image classification opens doors to a wide range of career opportunities. Moreover, with the exponential growth of digital imagery in fields such as healthcare, autonomous vehicles, agriculture, and more, the demand for experts in image classification continues to soar.Course Breakdown:You will use Google Colab notebooks for writing the python code for image classification using Deep Learning models. </l
Uncover the true potential of AI tools like ChatGPT, Gemini, and Claude AI through the art of prompt engineering! This beginner-friendly course is designed for everyone—whether you're a executive, student, teacher, marketer, entrepreneur, manager, software engineering or just curious of AI. You'll learn how to communicate effectively with Generative AI to get smarter, faster, and more accurate results there by increasing your productivity.Through engaging, bite-sized lessons and real-world demos, you’ll master prompt types, structuring, personas, creativity control, debugging, and advanced techniques like Chain-of-Thought, Few-Shot, and ReAct. No coding skills needed!Learn how to:- Write prompts that get meaningful results- Improve outputs by refining and structuring input- Use AI for writing, learning, marketing, coding, and more- Apply prompts ethically and responsibly- Build your own AI-powered workflowsPerfect for anyone looking to become confident in using AI tools like a pro.Each Section is well made with:- Engaging contents and real-world analogies- Short videos per lecture- Hands-on demos in every module- Assignments to apply what you've learned- Beginner-friendly language throughoutTools and Technologies Covered:OpenAI, ChatGPT, Gemini, Perplexity, Claude, Google Colob, LangChain Template, Python Coding for non-coders, Python NotebookFAQs:What is prompt engineering, and why should I learn it?Prompt engineering is the skill of writing effective prompts to guide AI tools like ChatGPT, Gemini, and Claude. It helps you get smarter, more accurate results from generative AI. With AI now integrated into productivity, education, marketing, and business, prompt engineering is quickly becoming a must-have skill for everyone — no coding required.Do I need a technical or coding background to
Master Deep Learning with Python for AI ExcellenceCourse Description: This meticulously crafted course is designed to empower you with comprehensive knowledge and practical skills to thrive in the world of artificial intelligence.Immerse yourself in engaging lectures and hands-on lab sessions that cover fundamental concepts, cutting-edge methodologies, and real-world applications of deep learning. Gain expertise in essential Python libraries, machine learning algorithms, and advanced techniques, setting a solid foundation for your AI career.Course Highlights:In-Demand Skills: Acquire the highly sought-after skills demanded by today's AI-centric job market, opening doors to data science, machine learning, and AI development roles.Hands-On Learning: Learn by doing! Our interactive lab sessions ensure you gain practical experience, from data preprocessing to model evaluation, making you a proficient deep learning practitioner.Comprehensive Curriculum: From foundational Python libraries like Pandas and NumPy to cutting-edge neural network architectures like CNNs and RNNs, this course covers it all. Explore linear regression, logistic regression, decision trees, clustering, anomaly detection, and more.Expert Guidance: Our experienced instructors are committed to your success. Receive expert guidance, personalized feedback, and valuable insights to accelerate your learning journey.Project-Based Learning: Strengthen your skills with real-world projects that showcase your <
This course provides an introduction to generative AI, covering topics like machine learning, virtual environments, and responsible AI.
Dive into the transformative world of generative AI with "Mastering Deep Learning for Generative AI." This comprehensive course is designed for aspiring data scientists, tech enthusiasts, and creative professionals eager to harness the power of deep learning to create innovative generative models.What You'll Learn:Foundations of Deep Learning: Understand the core principles of neural networks, including supervised and unsupervised learning.Generative Models: Master the building and training of advanced generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers.Hands-On Projects: Engage in practical projects that guide you through creating applications in art, music, text, and design using generative AI.Model Optimization: Learn techniques to evaluate, improve, and fine-tune the performance of your generative models for real-world applications.Ethical Considerations: Explore the ethical implications and future impact of generative AI, ensuring responsible and informed application of these technologies.Course Highlights:Comprehensive Learning: From fundamentals to advanced concepts, gain a robust understanding of deep learning for generative AI.Practical Experience: Hands-on projects provide real-world experience, enhancing your ability to apply what you learn.Cutting-Edge Techniques: Stay ahead with the latest advancements in generative AI technologies.Expert Guidance: Learn from experienced instructors who provide clear explanations and valuable insights.Who Should Enroll:Aspiring Data Scientists: Those looking to specialize in deep learning a
"This course contains the use of artificial intelligence in creating scripts, visuals, audio, and supporting content"This 8-week course is a complete foundation in Generative AI and Large Language Models (LLMs), designed to help you build both conceptual understanding and practical skills. The program is structured to gradually move from the basics of generative models to advanced applications, customization, safety, and a capstone project that showcases your abilities. The course begins with an introduction to Generative AI, where you will explore tokenization, attention mechanisms, and the transformer architecture that forms the backbone of modern LLMs. You will learn how text generation works, experiment with prompt design, and analyze the impact of model parameters like temperature and top-p on creativity and accuracy. Building on this, the course dives into the foundations of large language models, exploring embeddings, perplexity, and context windows. You will also study core generative models such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and diffusion models, gaining an intuitive understanding of how these models generate text, images, and structured data. The practical modules allow you to apply Generative AI in practice, including summarization, creative writing, code generation, data augmentation, and image synthesis. You will use modern <strong
This course will teach you Deep learning focusing on Convolution Neural Net architectures. It is structured to help you genuinely learn Deep Learning by starting from the basics until advanced concepts. We will begin learning what it is under the hood of Deep learning frameworks like Tensorflow and Pytorch, then move to advanced Deep learning Architecture with Pytorch.During our journey, we will also have projects exploring some critical concepts of Deep learning and computer vision, such as: what is an image; what are convolutions; how to implement a vanilla neural network; how back-propagation works; how to use transfer learning and more.All examples are written in Python and Jupyter notebooks with tons of comments to help you to follow the implementation. Even if you don’t know Python well, you will be able to follow the code and learn from the examples.The advanced part of this project will require GPU but don’t worry because those examples are ready to run on Google Colab with just one click, no setup required, and it is free! You will only need to have a Google account. By following this course until the end, you will get insights, and you will feel empowered to leverage all recent innovations in the Deep Learning field to improve the experience of your projects.
Are you ready to dive into the world of Machine Learning and Artificial Intelligence? This comprehensive Machine Learning & AI Bootcamp will take you from beginner to advanced, equipping you with the skills to build intelligent applications, automate decision-making, and apply AI to real-world challenges.What You Will LearnUnderstand Machine Learning fundamentals, including supervised and unsupervised learning.Master essential Python libraries like NumPy, Pandas, and Scikit-learn for data analysis.Implement regression and classification models for predictive analytics.Explore ensemble learning techniques such as Random Forest and Gradient Boosting.Work with clustering algorithms for unsupervised learning.Learn dimensionality reduction techniques like PCA and t-SNE.Build Natural Language Processing (NLP) models for text analysis and chatbots.Apply Computer Vision for image recognition and object detection.Understand search and optimization techniques in AI.Develop AI-powered applications using Generative AI and Reinforcement Learning.Work on real-world projects, including AI chatbots, fraud detection, and recommendation systems.Who Is This Course For?Beginners looking to build a solid foundation in Machine Learning and AI.Developers and Data Scientists wanting to implement AI-driven solutions.Tech enthusiasts eager to explore NLP, Computer Vision, and AI automation.Business professionals seeking to leverage AI in decision-making and
Led by GP, a distinguished AI researcher with 11 PubMed publications and a rich academic background from Cornell, UCSF, NIH, and Amherst College, this course spans the essentials of web development to the frontiers of AI technology. Dive into a learning experience with LIVE HELP available Monday to Friday, 9-5, plus additional online support.Our curriculum is in constant evolution, tailored to your feedback and the dynamic landscape of machine learning and AI. This isn't just another bootcamp; it's a bridge from foundational HTML to pioneering in Python 3, Machine Learning, TensorFlow, and beyond into Artificial Intelligence and Recurrent Neural Networks.Designed for rapid learning, we break down complex concepts into manageable steps. Starting from HTML and CSS to Bootstrap and JavaScript, and advancing through Python 3 to data science, machine learning, and AI, we cover ground rapidly but solidly.Expect to delve into:Frontend web technologies: HTML, CSS, Bootstrap, JavaScript, jQueryPython programming essentials and advanced conceptsData Science, including Machine Learning and AI with TensorFlowPractical applications with projects in sentiment analysis, regression, clustering, and neural networksAn exploration of both traditional statistics and machine learning techniquesWith over 170 lectures and 30+ hours of video content, this course is your most comprehensive guide to becoming a proficient Python developer and an AI specialist. You'll get lifetime access to all materials, including lecture Notebooks.This course is perfect for beginners with no prior programming experience, bootcamp graduates looking to tackle real-world projects, and intermediate Python programmers eager to master AI programming. With a 30-day money-back guarantee, there's no risk in taking the leap. Transform your career with the skills to thrive in the era of AI.
A beginner-friendly course exploring how Generative AI is transforming supply chain management, covering applications in demand forecasting, inventory optimization, and logistics through practical insights and case studies.
Dive into the realm of Artificial intelligence and master Deep Learning with our comprehensive course, "Master Deep Learning in 2023: A Comprehensive Bootcamp"Are you fascinated by the power and potential of artificial intelligence, machine learning and deep learning? Are you looking for a comprehensive and immersive way to learn about Deep Learning? If so, then this course is for you!Designed with both beginners and professionals in mind, this course offers a deep and engaging journey into the field of AI and deep learning. With a focus on deep learning, you'll explore the latest and most impactful techniques and technologies in this dynamic and rapidly evolving field.Our course begins by providing a strong grounding in the fundamental concepts of AI and deep learning. You'll learn the basics of neural networks, deep learning frameworks, and more. With this solid foundation, we'll then move on to explore more complex topics such as convolutional and recurrent neural networks, long short-term memory (LSTMs), pre-trained models & Transfer Learning.Throughout the course, you'll benefit from practical examples and real-world case studies to help you connect theoretical concepts to practical applications. You'll also complete hands-on projects to help you apply your learning to the most pressing challenges facing AI and deep learning today.But our course doesn't simply prepare you to apply deep learning techniques in the real world--it also equips you with the ethical considerations and implications of using AI. You'll learn about critical issues like bias and fairness in machine learning, and develop your ability to think critically about the challenges and opportunities presented by these new technologies.By the end of the course, you'll have a comprehensive understanding of deep learning and the skills to apply these techniques to rea
Google's fast-paced, practical introduction to machine learning. A self-study guide for aspiring machine learning practitioners.
This microlearning course provides a foundational understanding of Vertex AI, guiding learners through the platform's interface and its core components. The curriculum is designed to impart strategic insights into how Vertex AI can be effectively utilized in various projects. It is suitable for aspiring data scientists, machine learning enthusiasts, and professionals looking to leverage the power of AI. The course covers the basics of Vertex AI, its key features, and its role in the MLOps process.
Hello there,Welcome to the “Artificial Intelligence with Machine Learning, Deep Learning ” courseArtificial intelligence, Machine learning python, python, machine learning, Django, ethical hacking, python Bootcamp, data analysis, machine learning python, python for beginners, data science, machine learning, DjangoArtificial Intelligence (AI) with Python Machine Learning and Python Deep Learning, Transfer Learning, TensorflowIt’s hard to imagine our lives without machine learning Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical modelsAi, TensorFlow, PyTorch, scikit learn, reinforcement learning, supervised learning, teachable machine, python machine learning, TensorFlow python, ai technology, azure machine learning, semi-supervised learning, deep neural network, artificial general intelligenceMachine learning isn’t just useful for predictive texting or smartphone voice recognition Machine learning is constantly being applied to new industries and new problems Whether you’re a marketer, video game designer, or programmer, my course on Udemy is here to help you apply machine learning to your work Data Science Careers Are Shaping The FutureData science experts are needed in almost every field, from government security to dating apps Millions of businesses and government departments rely on big data to succeed and better serve their customers So data science careers are in high demandUdemy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies Whether you’re interested in machine learning, data mining, or data analysis, Udemy has a course for youIf you want to learn one of the
Das sagen Teilnehmer über diesen Kurs:"Sehr aktiver Dozent der sich um die Kursteilnehmer und den Kurs kümmert. Der Tensorflow Kurs hat viele beispiele was mir geholfen hat Tensorflow und Keras besser zu verstehen. Ebenfalls sehr gut waren auch die Begriff erklärungen die einem sehr helfen ML als beginner zu lernen." - Ibrahim Akkulak"Ich würde den Kurs auf jeden Fall weiter empfehlen. Mehr Content als gedacht und sehr viele Erklärungen. Top!" - Erik Andrè Thürsam"Der Kurs gefällt mir ganz gut und bringt viele Beispiele ein. Der Saif beantwortet Fragen super schnell und ist sehr hilfsbereit. Empfehle den Kurs sehr für alle die Deep Learning mit vielen Praxisbeispielen lernen möchten." - Simon BehrensDeep Learning ist eines der angesagtesten Themen weit und breit. Insbesondere wird Deep Learning und Künstliche Neuronale Netze in vielen Technologien in deinem Umfeld eingesetzt, um dir ein noch angenehmeres Leben zu ermöglichen. Mithilfe diesen Praxis-Kurs bringe ich dir bei wie man Deep Learning mithilfe von Keras, Tensorflow und Python einsetzt. Du wirst eine gute Mischung von Theorie und Praxis in diesen Kurs erhalten. Viele der Techniken werden anhand von echten Praxis Projekte dir vermittelt. Warum solltest du Keras lernen? Keras wird von den "Big Five" Unternehmen wie Apple, Google, Facebook, Amazon und Microsoft in vielen ihrer Produkte eingesetzt, um Machine Learning noch effizienter zu nutzen! Ebenfalls werde ich ihn auch immer auf dem neusten Stand der Technik und Wissenschaft halten. Lerne wie du Keras meisterst und schreibe dich JETZT ein!
This course provides an introduction to TensorFlow, one of the most popular deep learning frameworks. You'll learn the basics of building and training neural networks for computer vision tasks.
Lets learn basics to transform your career. I promise not to exhaust you with huge number of videos.Artificial Intelligence, Machine Learning, Data Science are the most hot skills in the markets which has potential to help you earn highest salary. These skills has potential to turn your financial to better level which can provide you growth and prosperity. Welcome to the most comprehensive Introduction to AI, Machine Learning and Data Science course! An excellent choice for beginners and professionals looking to expand their knowledge on Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Supervised and Unsupervised Learning. This is an introductory course for beginners to boost your knowledge. This course gives introduction to to AI, Machine Learning, Data Science, Deep Learning, Supervised and Unsupervised learning with real time examples where machine learning can be applied to solve or simplify real world business problems.What you'll learnIntroduction to buzz words like AI, Machine Learning, Data Science and Deep Learning etc.Real time examples where Machine Learning can be used to solve real world business problemsIntroduction to Supervised Learning and Unsupervised LearningIntroduction to Natural Language ProcessingWhy python is popular for Machine LearningPrerequisite: You just need computer or mobile phone with internet connection to access course material.No prerequisites !Happy Learning!
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.Welcome to Tensorflow 2.0!What an exciting time. It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version.Tensorflow is Google's library for deep learning and artificial intelligence.Deep Learning has been responsible for some amazing achievements recently, such as:Generating beautiful, photo-realistic images of people and things that never existed (GANs)Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)Self-driving cars (Computer Vision)Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning)Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.In other words, if you want to do deep learning, you gotta know Tensorflow.This course is for beginner-level students all the way up to expert-level students. How can this be?If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.Along the way, you will learn about
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. Neural networks are one of the staples of machine learning, and they are always a top contender in Kaggle contests. If you want to improve your skills with neural networks and deep learning, this is the course for you.You already learned about backpropagation, but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time.You will also learn about momentum, which can be helpful for carrying you through local minima and prevent you from having to be too conservative with your learning rate. You will also learn about adaptive learning rate techniques like AdaGrad, RMSprop, and Adam which can also help speed up your training.Because you already know about the fundamentals of neural networks, we are going to talk about more modern techniques, like dropout regularization and batch normalization, which we will implement in both TensorFlow and Theano. The course is constantly being updated and more advanced regularization techniques are coming in the near future.In my last course, I just wanted to give you a little sneak peak at TensorFlow. In this course we are going t
This course covers the basics of unsupervised learning, supervised learning, reinforcement learning algorithms, and generative models with applications in electric power systems.
Python is famed as one of the best programming languages for its flexibility. It works in almost all fields, from web development to developing financial applications. However, it's no secret that Python’s best application is in deep learning and artificial intelligence tasks.While Python makes deep learning easy, it will still be quite frustrating for someone with no knowledge of how machine learning works in the first place.If you know the basics of Python and you have a drive for deep learning, this course is designed for you. This course will help you learn how to create programs that take data input and automate feature extraction, simplifying real-world tasks for humans.There are hundreds of machine learning resources available on the internet. However, you're at risk of learning unnecessary lessons if you don't filter what you learn. While creating this course, we've helped with filtering to isolate the essential basics you'll need in your deep learning journey.It is a fundamentals course that’s great for both beginners and experts alike. If you’re on the lookout for a course that starts from the basics and works up to the advanced topics, this is the best course for you.It only teaches what you need to get started in deep learning with no fluff. While this helps to keep the course pretty concise, it’s about everything you need to get started with the topic.
The course "AI Agents for Everyone and Artificial Intelligence Bootcamp" is designed to demystify the world of intelligent systems, making it accessible to learners of all levels. Whether you're a curious beginner or an aspiring AI developer, this course provides a comprehensive foundation in the development, deployment, and application of AI agents across various domains. With a strong emphasis on hands-on learning, participants will explore state-of-the-art technologies such as machine learning, natural language processing (NLP), and advanced frameworks like AutoGPT, IBM Bee, LangGraph, and CrewAI.Throughout the course, learners will gain a deep understanding of how AI agents function, from basic reflex agents to advanced collaborative systems. You'll learn about the core principles that govern intelligent agents, including decision-making, adaptability, and autonomy. By understanding these foundations, you will be equipped to create AI agents that can perceive their environment, make informed decisions, and perform complex tasks. The course also delves into the critical technologies that power AI agents, such as machine learning algorithms for predictive insights, NLP techniques for conversational AI, and robotics integration for automation.One of the course’s unique aspects is its focus on practical application. You will work on hands-on projects to develop and deploy AI agents in real-world scenarios. From creating collaborative systems with CrewAI to implementing stateful interactions using LangGraph, you’ll get valuable experience with cutting-edge tools and frameworks. Additionally, the course explores the transformative potential of AI agents in industries such as healthcare, finance, business operations, entertainment, and IoT, providing actionable insights into their role in shaping the future.Ethics and societal impact are integral to this learning experience. The course examines the ethical considerations and regulatory challenges surrounding AI agents,
كورس لتعليم اساسيات التعلم العميق والشبكات العصبية الالتفافية للمبتدئين وحتى المستوى المتقدمسواء كنت طالباً فى علوم الحاسب او طالباً فى الهندسة أو مبرمجاً وتعشق مجال الذكاء الاصطناعى , فإن هذا الكورس سيساعدك علي فهم أساسيات التعلم الشبكات العصبيه الالتفافية و الوصول إلى مستوى محترف وسوف يركز هذا الكورس على الجوانب النظرية وراء الخوارزميات والنماذج المنتشره هذه الايام للتعلم العميقThis course is focus on the theoretical aspects of the recent convolutional neural network based methods.######################################################################################################################################Section 1: Introduction to Convolutional Neural Network (CNN)Lecture 1: Introduction to Deep LearningLecture 2: ImageNet ChallengeLecture 3: Drawbacks of Previous Neural NetworksLecture 4: CNN Motivation & HistorySection 2: Convolutional Neural Network PropertiesLecture 5: Local ConnectivityLecture 6: Parameter SharingLecture 7: Pooling & SubsamplingSection 3: Convolution OperationLecture 8: Definition of ConvolutionLecture 9: Image Convolution ExampleLecture 10: Other FiltersSection 4: Convolutional Neural Network LayersLecture 11: Convolutional LayerLecture 12: Strided ConvolutionLecture 13: Strided Convolution with PaddingLecture 14: Convolution over VolumeLecture 15: Activation Function (ReLU)Lecture 16: Pooling LayerLecture 17: Convolutional NetworkLecture 18: BatchNormalization LayerSection 5: Convolutional Neural Network ArchitecturesLecture 19: Introduction to CNN ArchitecturesLecture 20: LeNet-5Lecture 21: AlexNet & ZFNetLecture 22: VGGNetLecture 23: GoogleNet (Inception Networ
IntroductionIntroduction of the CourseIntroduction to Machine Learning and Deep LearningIntroduction to Google ColabPython Crash CourseData PreprocessingSupervised Machine LearningRegression AnalysisLogistic RegressionK-Nearest Neighbor (KNN)Bayes Theorem and Naive Bayes ClassifierSupport Vector Machine (SVM)Decision TreesRandom ForestBoosting Methods in Machine LearningIntroduction to Neural Networks and Deep LearningActivation FunctionsLoss FunctionsBack PropagationNeural Networks for Regression AnalysisNeural Networks for ClassificationDropout Regularization and Batch NormalizationConvolutional Neural Network (CNN)Recurrent Neural Network (RNN)AutoencodersGenerative Adversarial Network (GAN)Unsupervised Machine LearningK-Means ClusteringHierarchical ClusteringDensity Based Spatial Clustering Of Applications With Noise (DBSCAN)Gaussian Mixture Model (GMM) ClusteringPrincipal Component Analysis (PCA)What you’ll learnTheory, Maths and Implementation of machine learning and deep learning algorithms.Regression Analysis.Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, Random Forest, and Boosting Methods in Machine Learning.Build Artificial Neural Networks and use them for Regression and Classification Problems.Using GPU with Deep Learning Models.Convolutional Neural NetworksTransfer LearningRecurrent Neural NetworksTime series forecasting and classification.AutoencodersGenerative Adversarial NetworksPython from scr
What is PyTorch and why should I learn it?PyTorch is a machine learning and deep learning framework written in Python.PyTorch enables you to craft new and use existing state-of-the-art deep learning algorithms like neural networks powering much of today’s Artificial Intelligence (AI) applications.Plus it's so hot right now, so there's lots of jobs available!PyTorch is used by companies like:Tesla to build the computer vision systems for their self-driving carsMeta to power the curation and understanding systems for their content timelinesApple to create computationally enhanced photography.Want to know what's even cooler?Much of the latest machine learning research is done and published using PyTorch code so knowing how it works means you’ll be at the cutting edge of this highly in-demand field.And you'll be learning PyTorch in good company.Graduates of Zero To Mastery are now working at Google, Tesla, Amazon, Apple, IBM, Uber, Meta, Shopify + other top tech companies at the forefront of machine learning and deep learning.This can be you.By enrolling today, you’ll also get to join our exclusive live online community classroom to learn alongside thousands of students, alumni, mentors, TAs and Instructors.Most importantly, you will be learning PyTorch from a professional machine learning engineer, with real-world experience, and who is one of the best teachers around!What will this PyTorch course be like?This PyTorch course is very hands-on and project based. You won't just be staring at your screen. We'll leave that for other PyTorch tutorials and courses.In this course you'll actually be:Running experimentsCompleting exercises to test your skillsBuilding real-world deep learning models and projects to mimic real life scenarios<
Are you ready to explore the limitless possibilities of Artificial Intelligence? In this course, Complete Artificial Intelligence: From Basics to Generative AI, we’ve designed a comprehensive journey to help you master the exciting world of AI and Machine Learning. Whether you're a beginner curious about AI or someone looking to enhance your skills, this course provides a clear and structured pathway for success.We start by covering the fundamentals of Artificial Intelligence, breaking down the concepts into simple, easy-to-follow lessons. You'll learn the basics of Machine Learning, including Supervised and Reinforcement Learning, and how these approaches are applied to solve real-world problems. As you progress, we move to Deep Learning, focusing on building Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) to tackle complex tasks like image and speech recognition.But that’s not all—we also take a deep dive into the rapidly growing field of Generative AI. From understanding how Generative Adversarial Networks (GANs) work to exploring cutting-edge technologies like Transformers and Large Language Models (LLMs), you’ll gain insights into how AI creates everything from realistic images to human-like text. Plus, we cover Natural Language Processing (NLP), explaining how AI understands and generates human language.Whether you're here to start a new career, enhance your knowledge, or simply explore AI, this course is your gateway to the future of technology. Enroll now and take the first step toward mastering the art and science of AI!
This course provides an introduction to graph representation learning, including matrix factorization-based methods, random-walk based algorithms, graph neural networks, and deep generative models of graphs. It covers both theoretical motivations and practical applications.
This is a top selling Machine Learning and Data Science course just updated this month with the latest trends and skills for 2023! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 900,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei’s courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Meta, + other top tech companies. You will go from zero to mastery!Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If
A comprehensive book that starts with beginner topics like graph theory and traditional graph approaches and moves to more advanced topics such as novel GNN models and state-of-the-art research. It is a self-contained resource with most of the required theory for graph neural networks.
This Course simplifies the advanced Deep Learning concepts like Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long Short Term Memory (LSTM), Gated Recurrent Units(GRU), etc. TensorFlow, Keras, Google Colab, Real World Projects and Case Studies on topics like Regression and Classification have been described in great detail. Advanced Case studies like Self Driving Cars will be discussed in great detail. Currently the course has few case studies.The objective is to include at least 20 real world projects soon. Case studies on topics like Object detection will also be included. TensorFlow and Keras basics and advanced concepts have been discussed in great detail. The ultimate goal of this course is to make the learner able to solve real world problems using deep learning. After completion of this course the Learner shall also be able to pass the Google TensorFlow Certification Examination which is one of the prestigious Certification. Learner will also get the certificate of completion from Udemy after completing the Course. After taking this course the learner will be expert in following topics. a) Theoretical Deep Learning Concepts.b) Convolutional Neural Networksc) Long-short term memoryd) Generative Adversarial Networkse) Encoder- Decoder Modelsf) Attention Modelsg) Object detectionh) Image Segmentationi) Transfer Learningj) Open CV using Pythonk) Building and deploying Deep Neural Networks l) Professional Google Tensor Flow developer m) Using Google Colab for writing Deep Learning coden) Python programming for Deep Neural NetworksThe Learners are advised to practice the Tensor Flow code as they watch the videos on Programming from this course. First Few sections have been uploaded, The course is in updation phase and the remaining sections will be added soon.
Academy of Computing & Artificial Intelligence proudly present you the course "Data Engineering with Python". It all started when the expert team of Academy of Computing & Artificial Intelligence (PhD, PhD Candidates, Senior Lecturers , Consultants , Researchers) and Industry Experts . hiring managers were having a discussion on the most highly paid jobs & skills in the IT/Computer Science / Engineering / Data Science sector in 2021. At the end of the Course you will be able to start your career in Data Mining & Machine Learning. 1) Introduction to Machine Learning - [A -Z] Comprehensive Training with Step by step guidance2) Setting up the Environment for Machine Learning - Step by step guidance [R Programming & Python]3) Supervised Learning - (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines (SVM), Random Forest)4) Unsupervised Learning5) Convolutional Neural Networks - CNN6) Artificial Neural Networks 7) Real World Projects with SourceCourse Learning OutcomesTo provide awareness of (Supervised & Unsupervised learning) coming under Machine Learning (Why we need Data Mining & Machine Learning, What is Data Mining, What is Machine Learning, Traditional Programming Vs Machine Learning, Steps to Solve a Data Mining & Machine Learning Problem, Classification , Clustering)Describe intelligent problem-solving methods via appropriate usage of Machine Learning techniques.To build appropriate neural models from using state-of-the-art python framework.To setup the Environment for Machine Learning - Step by step guidance [R Progra
Learn Statistics for Data Science and Business Analysis
Data Science , Machine Learning : Ultimate Course For AllCourse Description:Welcome to the ultimate Data Science , Machine Learning course for 2025 – your complete guide to mastering Data Science , Machine Learning from the ground up with real-world examples and hands-on projects.This course is designed for beginners and intermediate learners who want to dive deep into the fields of Data Science , Machine Learning. Whether you’re starting from zero or brushing up your skills, this course will walk you through all the essential concepts, tools, and techniques used in Data Science , Machine Learning today.You’ll begin by understanding the core principles of Data Science , Machine Learning, then move into Python programming, data preprocessing, model training, evaluation, and deployment. With step-by-step explanations and practical exercises, you’ll gain real-world experience in solving problems using Data Science , Machine Learning.By the end of the course, you’ll be fully equipped to handle real projects and pursue career opportunities in Data Science , Machine Learning confidently.Class Overview:Introduction to Data Science , Machine Learning:Understand the principles and concepts of data science and machine learning.Explore real-world applications and use cases of data science across various industries.Python Fundamentals for Data Science:Learn the basics of Python programming language and its libraries for data science, including NumPy, Pandas, and Matplotlib.Master data manipulation, analysis, and visualization techniques using Python.Data Preprocessing and Cleaning:Understan
This course is a non-technical introduction to the basics of machine learning, including supervised learning concepts.
Welcome to the most in-depth and engaging Machine Learning & Data Science Bootcamp designed to equip you with practical skills and knowledge for a successful career in the AI field. This comprehensive course is tailor-made for beginners and aspiring professionals alike, guiding you from the fundamentals to advanced topics, with a strong emphasis on Python programming and real-world applications.Become a master of Machine Learning, Deep Learning, and Data Science with Python in this comprehensive bootcamp. This course is designed to take you from beginner to expert, equipping you with the skills to build powerful AI models, solve real-world problems, and land your dream job in 2024.Master the fundamentals of Data Science:Learn how to work with data effectively, from collection and cleaning to analysis and visualization.Master essential Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and exploration.Discover the power of data preprocessing techniques to enhance your model's performance.Unlock the potential of Machine Learning with Python:Dive into the core concepts of machine learning algorithms, including regression, classification, and clustering.Implement popular ML algorithms using Scikit-learn, the go-to library for ML in Python.Build your own predictive models and evaluate their accuracy with real-world datasets.Launch your career in Data Science and Machine Learning:Gain practical experience by working on real-world projects and case studies.Learn how to deploy your models in production environments to create real-world impact.Prepare for technical interviews and land your dream job with career guidance and tips.Why choose this course:Comprehensive curriculum covering all essential aspects of
This beginner-level course introduces the concepts of linear algebra that are relevant to AI, machine learning, and deep learning.
This course provides a case-study based introduction to the foundational concepts of machine learning.
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This University of London course provides a comprehensive introduction to probability and statistics, focusing on understanding and interpreting p-values and confidence intervals.
Artificial intelligence is one of the most rapidly evolving fields in technology, and ChatGPT is at the forefront of this revolution. By understanding ChatGPT and its capabilities, you will have the knowledge to create innovative solutions in a wide range of industries, from finance and healthcare to education and entertainment.This course is designed to provide a comprehensive overview of ChatGPT for beginners who are interested in exploring the possibilities of artificial intelligence. ChatGPT is a powerful language model trained by OpenAI, based on the GPT-3.5 architecture, and is capable of generating human-like responses to a wide range of prompts.In this masterclass, you will learn how to use ChatGPT to generate text, including content creation and creating ebooks.In addition to its use for content creation and ebooks, ChatGPT has also become an essential tool for aspiring AI consultants. The course includes a section on how to maximize the potential of ChatGPT as an AI consultant, including best practices for working with clients and integrating the technology into existing workflows.Whether you are a beginner exploring the possibilities of AI or an experienced consultant looking to expand your skillset, this masterclass will provide you with a comprehensive understanding of ChatGPT and its potential applications.
Welcome to our Python & TensorFlow for Machine Learning complete course. This intensive program is designed for both beginners eager to dive into the world of data science and seasoned professionals looking to deepen their understanding of machine learning, deep learning, and TensorFlow's capabilities.Starting with Python—a cornerstone of modern AI development—we'll guide you through its essential features and libraries that make data manipulation and analysis a breeze. As we delve into machine learning, you'll learn the foundational algorithms and techniques, moving seamlessly from supervised to unsupervised learning, paving the way for the magic of deep learning.With TensorFlow, one of the most dynamic and widely-used deep learning frameworks, we'll uncover how to craft sophisticated neural network architectures, optimize models, and deploy AI-powered solutions. We don't just want you to learn—we aim for you to master. By the course's end, you'll not only grasp the theories but also gain hands-on experience, ensuring that you're industry-ready.Whether you aspire to innovate in AI research or implement solutions in business settings, this comprehensive course promises a profound understanding, equipping you with the tools and knowledge to harness the power of Python, Machine Learning, and TensorFlow.We're excited about this journey, and we hope to see you inside!
Take your first step towards becoming a data science expert with our comprehensive R programming course. This course is designed for beginners with little or no programming experience, as well as experienced R developers looking to expand their skill set.You'll start with the basics of R programming and work your way up to advanced techniques used in data science. Along the way, you'll gain hands-on experience with popular R libraries such as dplyr, ggplot2, and tidyr.You will learn how to import, clean and manipulate data, create visualizations and statistical models to gain insights and make predictions. You will also learn data wrangling techniques and how to use R for data visualization.By the end of the course, you'll have a solid understanding of R programming and be able to apply your new skills to a wide range of data science projects. You'll also learn how to use R in Jupyter notebook, so that you can easily share your work and collaborate with others.So, if you're ready to take your first step towards becoming a data science expert, this is the course for you! With our hands-on approach and interactive quizzes, you'll be able to put your new skills into practice right away.In this course, you learn:How to install R-PackagesHow to work with R-data typesWhat is R DataFrame, Matrices, Vectors, etc?How to work with DataFramesHow to perform join and merge operations on DataFramesHow to plot data using ggplot2 in R 4Analysis of real-life dataset Covid-19 How this course will help you?This course will give you a very solid foundation in machine learning. You will be able to use the concepts of this course in other machine learning models. If you are a business manager or an executive or a student who wants to learn and excel in machine learning, this is the perfect course for you.
This talk covers the basics of data governance, including the people, processes, and tools needed to automate data quality at scale. It addresses how to define data domains, organize data architecture, create data QA, and build more transparency into algorithms.
This course is designed for scientists, engineers, and other problem-solvers who want to learn the basics of statistical thinking and how to apply it to real-world problems. You will learn about data analysis, experimental design, and statistical modeling.
Master Deep Learning and Computer Vision: From Foundations to Cutting-Edge Techniques Elevate your career with a comprehensive deep dive into the world of machine learning, with a focus on object detection, image classification, and object tracking.This course is designed to equip you with the practical skills and theoretical knowledge needed to excel in the field of computer vision and deep learning. You'll learn to leverage state-of-the-art techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and advanced object detection models like YOLOv8.Key Learning Outcomes:Fundamental Concepts:Grasp the core concepts of machine learning and deep learning, including supervised and unsupervised learning.Understand the mathematical foundations of neural networks, such as linear algebra, calculus, and probability theory.Computer Vision Techniques:Master image processing techniques, including filtering, noise reduction, and feature extraction.Learn to implement various object detection models, such as YOLOv8, Faster R-CNN, and SSD.Explore image classification techniques, including CNN architectures like ResNet, Inception, and EfficientNet.Dive into object tracking algorithms, such as SORT, DeepSORT, and Kalman filtering.Practical Projects:Build real-world applications, such as license plate recognition, traffic sign detection, and sports analytics.Gain hands-on experience with popular deep learning frameworks like TensorFlow and PyTorch.Learn to fine-tune pre-trained models and train custom models for specific tasks.Why Choose This Course?Expert Instruction: Learn from experienced ins
You’ve just stumbled upon the most complete, in-depth Neural Networks for Classification course online.Whether you want to:- build the skills you need to get your first data science job- move to a more senior software developer position- become a computer scientist mastering in data science- or just learn Neural Networks to be able to create your own projects quickly....this complete Neural Networks for Classification Masterclass is the course you need to do all of this, and more.This course is designed to give you the Neural Network skills you need to become a data science expert. By the end of the course, you will understand the Multilayer Perceptron Neural Networks for Classification method extremely well and be able to apply them in your own data science projects and be productive as a computer scientist and developer.What makes this course a bestseller?Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.Like you, they were tired of low-quality lessons, poorly explained topics, and confusing info presented in the wrong way. That’s why so many find success in this complete Neural Networks for Classification course. It’s designed with simplicity and seamless progression in mind through its content.This course assumes no previous data science experience and takes you from absolute beginner core concepts. You will learn the core dimensionality reduction skills and master the Multilayer Perceptron (MLP) technique. It's a one-stop shop to learn Multilayer Networks. If you want to go beyond the core content you can do so at any time.What if I have questions?As if this course wasn’t com
This course emphasizes the importance of building reliable machine learning systems. It covers software testing basics applied to the ML domain to enhance the quality of ML applications. The curriculum includes different testing methodologies like unit and integration testing, as well as more advanced techniques designed for machine learning such as behavioral and smoke testing.
This course provides a comprehensive introduction to time series analysis and forecasting, covering widely used techniques like ETS and ARIMA with hands-on examples in Python.
This course explores how to use data-driven AI to enhance customer engagement and build a competitive advantage. It's a beginner-level course that can be completed in about one week.
This course is complete guide of AWS SageMaker wherein student will learn how to build, deploy SageMaker models by brining on-premises docker container and integrate it to SageMaker. Course will also do deep drive on how to bring your own algorithms in AWS SageMaker Environment. Course will also explain how to use pre-built optimized SageMaker Algorithm.Course will also do deep drive how to create pipeline and workflow so model could be retrained and scheduled automatically.This course covers all aspect of AWS Certified Machine Learning Specialty (MLS-C01)This course will give you fair ideas of how to build Transformer framework in Keras for multi class classification use cases. Another way of solving multi class classification by using pre-trained model like Bert .Both the Deep learning model later encapsulated in Docker in local machine and then later push back to AWS ECR repository.This course offers:AWS Certified Machine Learning Specialty (MLS-C01)What is SageMaker and why it is requiredSageMaker Machine Learning lifecycleSageMaker ArchitectureSageMaker training techniques:Bring your own docker container from on premise to SageMakerBring your own algorithms from local machine to SageMakerSageMaker Pre built AlgorithmSageMaker Pipeline developmentSchedule the SageMaker Training notebookMore than 5 hour course are provided which helps beginners to excel in SageMaker and will be well versed with build, train and deploy the models in SageMaker
Step into the world of Generative AI and Large Language Models (LLMs) with this Complete Generative AI Mastery Course, an immersive, project-driven program designed to take you from fundamentals to professional-level mastery. In this Generative AI course, you will build production-grade AI applications using industry-standard frameworks such as LangChain, LLaMA 3, FAISS, and Milvus — the same technologies powering real-world enterprise and research-grade AI systems.The course begins with a deep dive into the core concepts of Transformers, GANs, embeddings, and foundation models, helping you understand how modern generative models process and generate human-like content. You will then explore Retrieval-Augmented Generation (RAG), vector databases, and multimodal AI to create powerful, context-aware, and intelligent solutions for text, image, and video understanding.Through 12+ guided, hands-on projects, you will build:AI chatbots powered by LLMsIntelligent document retrieval & RAG systemsImage generation & Vision AI applicationsSemantic similarity search enginesAI-powered video retrieval systemsYou will work with cutting-edge models and architectures, including T5 and multimodal models, while applying best practices for real-world system design.By the end of this course, you will master the complete Generative AI pipeline — from data ingestion, embeddings, model chaining, fine-tuning, and optimization to scalable deployment across edge, cloud, and hybrid environments.Whether you are a Python developer, AI enthusiast, data scientist, researcher, or tech innovator, this course equips you with the <st
You will build a binary classification machine learning model to predict if a person is looking for a new job or not. You'll go through the end to end machine learning project-- data collection, exploration, feature engineering, model selection, data transformation, model training, model evaluation and model explainability. We will brainstorm ideas throughout each step and by the end of the project you'll be able to explain which features determine if someone is looking for a new job or not.The template of this Jupyter Notebook can be applied to many other binary classification use cases. Questions like -- will X or Y happen, will a user choose A or B, will a person sign up for my product (yes or no), etc. You will be able to apply the concepts learned here to many useful projects throughout your organization!This course is best for those with beginner to senior level Python and Data Science understanding. For more beginner levels, feel free to dive in and ask questions along the way. For more advanced levels, this can be a good refresher on model explainability, especially if you have limited experience with this. Hopefully you all enjoy this course and have fun with this project!
Hello and welcome to the Machine Learning with STATA course. Machine Learning is influencing our daily lives and is one of the most significant aspects of technological advancements. The goal of this course is to provide you with the most up-to-date Machine Learning methodologies using STATA . It will teach you how to think about data science and machine learning in a new way. This is an excellent approach to begin a career in Machine Learning because you will learn some fundamental principles and receive practical experience. I'm thrilled to share what I know about Machine Learning using STATA with you. I assure you that it will be well worth your time and effort, and that you will gain a vital skill.Based on our research this is the only course that uses STATA to apply Machine Learning Models in Credit Risk Scenario. Because we know that many of you are already familiar with STATA or want to be familiar, we chose it as our platform. From the beginning to the finish of the course, we will start from scratch and work together to build new abilities. In this course, we will work together to create a complete data science project utilizing Credit Risk Data from start to finish. For this course, we have information on around 40,000 consumers, including their level of education, age, marital status, where they live, if they own a home, and other pertinent information. We'll get our hands filthy with these numbers and dig deep into them, and you'll be able to practice on your own. Additionally, you will have access to essential resources like as lectures, homework, quizzes, slides, and a literature analysis on modeling methodologies. Let's see what the course structure looks like right now!
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Pytorch&Hugginface Deep Learning Course(Colab Hands-On) Welcome to Pytorch Deep Learning From Zero To Hero Series.If you have already mastered the basic syntax of python and don't know what to do next, this course will be a rocket booster to skyrocket your programming skill to a business applicable level.In this course, you will be able to master implementing deep neural network from the very beginning(simple perceptron) to BERT transfer learning/Google's T5 by using pytorch and huggingface yourself by colab. Each Section will have one assignment for you to think and code yourself. The Agenda is below. Agenda:IntroductionGoogle ColaboratoryNeuronPerceptronMake Your Perceptron TrainableNormalize DataActivation FunctionLoss FunctionGradient DescentElegant Pytorch Gradient DescentFinal ProjectFinal Project ExplainedMulti Layer Perceptron(MLP)One Hot EncodingPrepare data for MLPDefine MLPTrain & Evaluate MLPFinal Project for MLPFCNN ExplainedFCNN LOVE Letters Classification using MLPFinal Project For FCNNCNN ExplainedCNN Prepare data(Fashion MNIST) CNN Define Model CNN Train&Evaluate ModelCNNInferenceFinal Project For CNNRNN ExplainedRNN Prepare dataRNN Define ModelRNN Train ModelRNN InferenceBERT Sesame StreetBERT Prepare Data IMDBBERT Model definitionBERT Model TrainingBERT Model Evaluation<p
Another course in Harvard's Data Science Professional Certificate that introduces the basics of statistical inference using the R programming language.
A beginner-friendly course to acquire essential data literacy skills for the workplace. It covers mastering data terminology, discovering business insights through data analysis, and adopting a data-driven mindset.
Welcome to "Generative AI Basics for Beginners" - the perfect course for anyone excited to explore the world of Generative AI (GenAI) and its amazing potential. This course is designed to give you a solid understanding of basic concepts, practical uses in different industries, and hands-on practice. It’s ideal for anyone new to AI, professionals looking to learn more, or anyone simply curious about this cutting-edge technology.In this course, you'll learn the essentials of Generative AI, see how it’s used in real life, and get to work on practical projects. We’ve made sure the content is easy to follow, even if you’re starting from scratch.To enhance your learning experience even further, we provide lifetime support for any questions you may have. You can always reach out to us for help, no matter how long it’s been since you started the course. So, join us and see how Generative AI can help you grow your skills and open up new career opportunities.Why Dive into Generative AI?Generative AI is revolutionizing how we create content—from text and images to music—making it a powerful tool across various industries. Understanding this technology will not only enhance your career prospects but also allow you to innovate and solve complex problems with ease.Course Highlights:Beginner-Friendly: Designed specifically for those new to AI and machine learning, with clear and straightforward explanations.Hands-On Experience: Work on your own AI projects and acquire hands-on skills through interactive exercises.Industry-Relevant Knowledge: Discover how Generative AI is transforming areas such as software development and marketing through real-world applications.Expert Guidance: Benefit from the extensive expertise of the instructor in AI and cloud technologies.Ethical and Future-Oriented: Delve into AI's ethical implications and fut
Welcome to the "Mastering AI Conversations: ChatGPT 2025 Masterclass" on Udemy, where you will learn how to build intelligent chatbots and conversational agents that can engage in natural language conversations with humans.In this course, you will be taught by ChatGPT, a large language model trained by OpenAI, which has been developed to understand and respond to human language in a way that is natural and intuitive.Throughout the course, ChatGPT will guide you through the basics of natural language processing (NLP) and machine learning (ML), and show you how to build and train your own chatbot using the latest AI technologies.You will learn how to design conversational flows, handle user inputs, and generate responses that are both informative and engaging. You will also gain a deep understanding of the underlying technologies that power modern chatbots, such as neural networks, sequence-to-sequence models, and attention mechanisms.By the end of the course, you will have the skills and knowledge necessary to build your own AI-powered chatbot that can converse with users on a wide range of topics, from customer support to personal assistants.So if you're ready to take your AI conversational skills to the next level, enroll in the "Mastering AI Conversations: ChatGPT 2025 Masterclass" today and start building your own intelligent chatbot!
A comprehensive program by Google that equips learners with in-demand skills for project management, including Agile methodologies. The course now includes AI training from Google experts, teaching how to use AI for tasks like creating project charters, identifying risks, and improving communications. It is designed for beginners with no prior experience.
A comprehensive, beginner-level certificate program that covers the entire UX design process. It has been updated to include AI training from Google experts, teaching how to leverage AI in design.
Machine learning is an extremely hot area in Artificial Intelligence and Data Science. There is no doubt that Neural Networks are the most well-regarded and widely used machine learning techniques. A lot of Data Scientists use Neural Networks without understanding their internal structure. However, understanding the internal structure and mechanism of such machine learning techniques will allow them to solve problems more efficiently. This also allows them to tune, tweak, and even design new Neural Networks for different projects. This course is the easiest way to understand how Neural Networks work in detail. It also puts you ahead of a lot of data scientists. You will potentially have a higher chance of joining a small pool of well-paid data scientists. Why learn Neural Networks as a Data Scientist? Machine learning is getting popular in all industries every single month with the main purpose of improving revenue and decreasing costs. Neural Networks are extremely practical machine learning techniques in different projects. You can use them to automate and optimize the process of solving challenging tasks. What does a data scientist need to learn about Neural Networks? The first thing you need to learn is the mathematical models behind them. You cannot believe how easy and intuitive the mathematical models and equations are. This course starts with intuitive examples to take you through the most fundamental mathematical models of all Neural Networks. There is no equation in this course without an in-depth explanation and visual examples. If you hate math, then sit back, relax, and enjoy the videos to learn the math behind Neural Networks with minimum efforts. It is also important to know what types of problems can be solved with Neural Networks. This course shows different types of problems to solve using Neural Networks including clas
A comprehensive 10-course program designed for beginners to become job-ready AI developers in about six months. It covers building AI-powered applications and chatbots using Python, Flask, and JavaScript, with no prior programming experience required.
A beginner-friendly program that prepares for a career in cybersecurity, covering topics from network security to incident response and threat intelligence, with a focus on IBM tools.
An eight-course program designed to build job-ready skills in marketing analytics, including data collection, evaluation, visualization, and A/B testing. This certificate is suitable for beginners and covers the use of Meta Ads Manager.
Latest update: I will show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab.This course is a complete guide for setting up TensorFlow object detection api, Transfer learning and a lot moreI think what you’ll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover.Here is the details about the project.Here we will star from colab understating because that will help to use free GPU provided by google to train up our model.We’re going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as ResNet, and Inception.We will understand object detection modules in detail using both tensorflow object detection api as well as YOLO algorithms.We’ll be looking at a state-of-the-art algorithm called RESNET and MobileNetV2 which is both faster and more accurate than its predecessors.One best thing is you will understand the core basics of CNN and how it converts to object detection slowly.I hope you’re excited to learn about these advanced applications of CNNs Yolo and Tensorflow, I’ll see you in class!AMAGING FACTS:· This course give’s you full hand’s on experience of training models in colab GPU.· Instead of focusing on the detailed inner workings of CNNs (which we've already done), we'll focus on high-level building blocks. The result? Almost zero math.· Another result? No complicated low-level code such as that written in Tensorflow, Theano,YOLO, or PyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written for you.
An industry-focused introduction to machine learning that covers key algorithms, data preparation techniques, and model evaluation strategies. It is ideal for those looking to apply ML in a business context.
This course is designed for anyone interested in pursuing a career in artificial intelligence and computer vision or looking to implement computer vision applications in their projects. In "Computer Vision Smart Systems: Python, YOLO, and OpenCV -1," we start with the fundamentals of computer vision and cover image processing techniques using the Python programming language and OpenCV library. Then, we advance to object detection and deep learning modeling using the YOLO (You Only Look Once) algorithm. Students will learn to build custom deep learning models from scratch, work with datasets, perform object detection, and apply these models in various projects.Throughout the course, practical exercises are provided step-by-step along with theoretical knowledge, giving students the chance to apply what they've learned. Additionally, we address common challenges you may face and provide detailed solutions. Aimed at building skills from basic to intermediate levels, this course serves as a comprehensive guide for anyone interested in the field of computer vision. It empowers you to develop smart systems for your projects and enhances your expertise in this exciting domain."You are never too old to set another goal or to dream a new dream." - C.S.Lewis"Do the difficult things while they are easy and do the great things while they are small. A journey of a thousand miles begins with a single step" - Lao TzuYou get the best information that I have compiled over years of trial and error and experience!Best wishes,Yılmaz ALACA
Master Real-Time Object Detection with Deep LearningDive into the world of computer vision and learn to build intelligent video analytics systems. This comprehensive course covers everything from foundational concepts to advanced techniques, including:Video Analytics Basics: Understand the 3-step process of capturing, processing, and saving video data.Object Detection Powerhouse: Explore state-of-the-art object detection models like Haar Cascade, HOG, Faster RCNN, R-FCN, SSD, and YOLO.Real-World Applications: Implement practical projects like people footfall tracking, automatic parking management, and real-time license plate recognition.Deep Learning Mastery: Learn to train and deploy deep learning models for image classification and object detection using frameworks like TensorFlow and Keras.Hands-On Experience: Benefit from line-by-line code walkthroughs and dedicated support to ensure a smooth learning journey.Exciting News!We've just added two new, hands-on projects to help you master real-world computer vision applications:Real-Time License Plate Recognition System Using YOLOv3: Dive deep into real-time object detection and recognition.Training a YOLOv3 Model for Real-Time License Plate Recognition: Learn to customize and train your own YOLOv3 model. Don't miss this opportunity to level up your skills!Why Enroll?Industry-Relevant Skills: Gain in-demand skills to advance your career in AI and machine learning.Practical Projects: Build a strong portfolio with real-world applications.Expert Guidance: Learn from experienced instructors and get personalized support.
This course covers the application of AI and computer vision in agriculture. It includes modules on emerging technologies in agriculture, crop-related applications like disease and nutrient deficiency detection, weather prediction, and livestock monitoring. The course provides over 50 hours of content, including code and datasets, and is beginner-friendly.
This comprehensive course will take you on a journey from the foundational concepts of machine learning and TensorFlow to the creation of advanced, real world deep learning models. I'll start with the basics, giving you a solid understanding of how neural networks work, and progressively build up your skills to tackle complex problems in computer vision, natural language processing (NLP), and more. Through a series of hands-on labs, projects, and practical examples, you'll learn to not only build and train models but also to understand the "why" behind the code, enabling you to confidently solve new and challenging problems.This course is designed for anyone with a basic understanding of Python programming who wants to build a career in machine learning and artificial intelligence. Whether you're a student, a software developer, or a data analyst, this course will provide you with the practical skills and foundational knowledge to become a proficient TensorFlow practitioner.Why Take This Course?Artificial Intelligence is transforming industries worldwide, and deep learning lies at its core. TensorFlow, developed by Google, has become the industry standard library for building and deploying AI applications at scale. This course provides a step by step learning journey, blending theory with hands-on coding so you not only understand concepts but can also implement them in real world projects.By the end of this course, you’ll have the knowledge and confidence to:Understand the foundations of deep learning and TensorFlow.Build simple and complex neural networks from scratch.Train, evaluate, and optimize models using modern techniques.Work with Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and advanced architectures.Deploy machine learning models in real-world scenarios.What You’ll L
Do you want to learn about Web Development and Machine learning at the same time? With this course you can do exactly that and more!This course was funded by a wildly successful KickstarterWith the Deep Learning of Angular 2 and Tensorflow, You will learn about Javascript frameworks for creating websites and create Apps driven by Machine Learning by learning Tensorflow as well as PyCharm, Python, Android Studio and more!About Tensorflow: We use frameworks like TensorFlow that make it easy to build, train, test, and use machine learning models. TensorFlow makes machine learning so much more accessible to programmers everywhereYou can expect a complete and comprehensive course that guides you first through the basics, then through some simple models. You will end up with a portfolio of apps driven by machine learning, as well as the know-how to create more and expand upon what we build together.About Angular 2: JavaScript is one of the fundamental languages of the web. JavaScript is easy to program in but some tasks are difficult. JavaScript frameworks are built to make these difficult tasks easier. In this course you will learn how to code with Angular.js 2, a powerful framework that makes building web apps a breeze. In this course you will learn web programming fundamentals and other valuable skill boosting career knowledge.This course is project based so you will not be learning a bunch of useless coding practices. At the end of this course you will have real world apps to use in your portfolio. We feel that project based training content is the best way to get from A to B. Taking this course means that you learn practical, employable skills immediately.Also, now included in this course are bonus courses of other related topics, such as C# and Java! You get more content at a great price!En
Are you short on time to start from scratch to use deep learning to solve complex problems involving topics like neural networks and reinforcement learning? Than this course is for you!This course is designed to help you to overcome various data science problems by using efficient deep learning models built in TensorFlow. You will begin with a quick introduction to TensorFlow essentials. Next, you start with deep neural networks for different problems and also explore the applications of Convolutional Neural Networks on two real datasets. We will than walk you through different approaches to RL. You’ll move from a simple Q-learning to a more complex, deep RL architecture and implement your algorithms using Tensorflow’s Python API. You’ll be training your agents on two different games in a number of complex scenarios to make them more intelligent and perceptive.By the end of this course, you’ll be able to implement RL-based solutions in your projects from scratch using Tensorflow and Python. Also you will be able to develop deep learning based solutions to any kind of problem you have, without any need to learn deep learning models from scratch, rather using tensorflow and it’s enormous power.Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Hands-on Deep Learning with TensorFlow is designed to help you to overcome various data science problems by using efficient deep learning models built in TensorFlow.The course begins with a quick introduction to TensorFlow essentials. Next, we start with deep neural networks for different problems and then explore the applications of Convolutional Neural Networks on two real datasets. If you’re facing time series problem then we will show you how to tackle it using RNN. We will also highlight how autoencoders can be used for efficient data representation. Lastly, we will take you through some of th
Welcome to our Data Science and Machine Learning course, meticulously crafted for those passionate about leveraging data and developing sophisticated models. This program starts with the fundamentals of data science, where you'll learn to collect, clean, and analyze data using Python libraries like pandas and NumPy. We’ll cover essential data visualization techniques to transform raw data into meaningful insights that drive decision-making.As you advance, we will delve into a range of machine learning algorithms, including both supervised and unsupervised methods. You'll gain hands-on experience with practical applications such as regression, classification, clustering, and dimensionality reduction. Our approach ensures that you not only understand theoretical concepts but also apply them to real-world scenarios through engaging projectsThe culmination of the course involves building a stock prediction tool, allowing you to apply your accumulated knowledge to a practical problem. This final project will showcase your ability to develop, implement, and evaluate predictive models, demonstrating your readiness for real-world challenges. By the end of this course, you'll possess a solid foundation in data science and machine learning, equipping you to tackle complex challenges and make valuable contributions in any industry. Join us to unlock your potential and advance your career in this dynamic and rapidly evolving field!
This course is designed by an industry expert who has over 2 decades of IT industry experience including 1.5 decades of project/ program management experience, and over a decade of experience in independent study and research in the fields of Machine Learning and Data Science.The course will equip students with a solid understanding of the theory and practical skills necessary to learn machine learning models and data science.When building a high-performing ML model, it’s not just about how many algorithms you know; instead, it’s about how well you use what you already know.Throughout the course, I have used appealing visualization and animations to explain the concepts so that you understand them without any ambiguity.This course contains 9 sections: 1. Introduction to Machine Learning 2. Anaconda – An Overview & Installation 3. JupyterLab – An Overview 4. Python – An Overview 5. Linear Algebra – An Overview 6. Statistics – An Overview 7. Probability – An Overview 8. OOPs – An Overview 9. Important Libraries – An OverviewThis course includes 20 lectures, 10 hands-on sessions, and 10 downloadable assets.By the end of this course, I am confident that you will outperform in your job interviews much better than those who have not taken this course, for sure.
Learn the Most demanding language of industry with concept applied to Data Science, Machine Learning and AIImportant topics are covered such as Python Basic Concepts, Advance Concept, Python Crash Course, Python Libraries such as numpy, pandas, matplotlib, seaborn, Data Science Concept with Case Studies , Machine Learning and it's types, Artificial Intelligence with Case Studies This Course will design to understand Data Visualization and Data Analysis with Machine Learning Algorithms with case Studies. Data Analysis with Machine Learning Algorithms such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered. The course provides path to start career in Data Analysis. Importance of Data, Collection of Data with Case Study is covered. Machine Learning Types such as Supervise Learning, Unsupervised Learning, are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered. The course provides path to start career in Data Analysis. Importance of Data, Collection of Data with Case Study is covered. Machine Learning Types such as Supervise Learning, Unsupervised Learning, are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered. Data Visualization and Analysis with ML using Python, Numpy Pandas, Matplotlib, Seaborn, Plotly & Scikit Learn libraryThis Course will design to understand Machine Learning Algorithms with case Studies using Scikit Learn Library. The Machine Learning Algorithms such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the trad
This course teaches machine learning from the basics so that you can get started with created amazing machine learning programs. With a well structured architecture, this course is divided into 4 modules:Theory section: It is very important to understand the reason of learning something. The need for learning machine learning and javascript in this particular case is explained in this section.Foundation section: In this section, most of the basic topics required to approach a particular problem are covered like the basics of javascript, what are neural networks, dom manipulation, what are tensors and many more such topicsPractice section: In this section, you put your learnt skills to a test by writing code to solve a particular problem. The explanation of the solution to the problem is also provided in good detail which makes hands-on learning even more efficient. Project section: In this section, we build together a full stack project which has some real life use case and can provide a glimpse on the value creation by writing good quality machine learning programsHappy Coding,Vinay Phadnis :)
This course will help you in understanding of the Linear Algebra and math’s behind Data Science and Machine Learning. Linear Algebra is the fundamental part of Data Science and Machine Learning. This course consists of lessons on each topic of Linear Algebra + the code or implementation of the Linear Algebra concepts or topics.There’re tons of topics in this course. To begin the course:We have a discussion on what is Linear Algebra and Why we need Linear AlgebraThen we move on to Getting Started with Python, where you will learn all about how to setup the Python environment, so that it’s easy for you to have a hands-on experience.Then we get to the essence of this course;Vectors & Operations on VectorsMatrices & Operations on MatricesDeterminant and InverseSolving Systems of Linear EquationsNorms & Basis VectorsLinear IndependenceMatrix FactorizationOrthogonalityEigenvalues and EigenvectorsSingular Value Decomposition (SVD)Again, in each of these sections you will find Python code demos and solved problems apart from the theoretical concepts of Linear Algebra.You will also learn how to use the Python's numpy library which contains numerous functions for matrix computations and solving Linear Algebric problems.So, let’s get started….
This course is ideal for you if you want to gain knowledge in statistical methods required for Data Science and machine learning!Learning Statistics is an essential part of becoming a professional data scientist. Most data science learners study python for data science and ignore or postpone studying statistics. One reason for that is the lack of resources and courses that teach statistics for data science and machine learning.Statistics is a huge field of science, but the good news for data science learners is that not all statistics are required for data science and machine learning. However, this fact makes it more difficult for learners to study statistics because they are not sure where to start and what are the most relevant topics of statistics for data science.This course comes to close this gap.This course is designed for both beginners with no background in statistics for data science or for those looking to extend their knowledge in the field of statistics for data science.I have organized this course to be used as a video library for you so that you can use it in the future as a reference. Every lecture in this comprehensive course covers a single topic.In this comprehensive course, I will guide you to learn the most common and essential methods of statistics for data analysis and data modeling.My course is equivalent to a college-level course in statistics for data science and machine learning that usually cost thousands of dollars. Here, I give you the opportunity to learn all that information at a fraction of the cost! With 77 HD video lectures, many exercises, and two projects with solutions.All materials presented in this course are provided in detailed downloadable notebooks for every lecture.Most students focus on learning python codes for data science, however, this is not enough to be a proficient data scientis
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!This course is made to give you all the required knowledge at the beginning of your journey, so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips and trick you would require to start your career.It gives detailed guide on the Data science process involved and Machine Learning algorithms. All the algorithms are covered in detail so that the learner gains good understanding of the concepts. Although Machine Learning involves use of pre-developed algorithms one needs to have a clear understanding of what goes behind the scene to actually convert a good model to a great model.Our exotic journey will include the concepts of:Comparison between Artificial intelligence, Machine Learning, Deep Learning and Neural Network.What is data science and its need.The need for machine Learning and introduction to NLP (Natural Language Processing).The different types of Machine Learning – Supervised and Unsupervised Learning.Hands-on learning of Python from beginner level so that even a non-programmer can begin the journey of Data science with ease.All the important libraries you would need to work on Machine learning lifecycle.Full-fledged course on Statistics so that you don’t have to take another course for statistics, we cover it all.Data cleaning and exploratory Data analysis with all t
This book provides a practical introduction to exploratory data analysis using Python. It covers topics such as distributions, probability, and hypothesis testing. The book is very hands-on and includes many case studies.
Today we see AI all around us.From apps on our phone, to voice assistants in our room, we have gadgets powered by AI and Machine Learning.If you’re curious to know how machine learning works, or want to get started with this technology, then this course is for you.This is a beginner level course in AI - Machine Learning and Deep Learning.As students, you will gain immensely by knowing about this transformative technology, its potential and how to make the best use of it. It will open up opportunities in your existing jobs as well as prepare you for new careers.It will go over the basic concepts, introduce the terminology and discuss popular Machine Learning and Deep Learning algorithms using examples.It will be ideal for•Students aspiring to begin a career in AI•IT Professionals and Managers who want to understand the basic concepts•Just about anyone who is curious to learn about AIAt the end of this course, you will•Understand the basic concepts and terminologies in Machine Learning•Gain intuition about how various Machine Learning and Deep Learning algorithms work•Learn how to use Machine Learning to solve a business problem•Be able to apply this knowledge to pursue a vendor certificationAre there any pre-requisites?Students must have a basic knowledge of undergraduate level mathematics in areas like Linear Algebra, Probability, Statistics and Calculus. The course will provide a basic refresher on these concepts.How much programming is needed?Although there are labs in the course, they are optional. You can go through the course without doing any programming. However, a basic knowledge of Computer Science and programming would help.The algorithms discussed in the course will be shown using pseudo code.We have an optional mo
Want to dive into Deep Learning and can't find a simple yet comprehensive course?Don't worry you have come to the right place.We provide easily digestible lessons with plenty of programming question to fill your coding appetite. All topic are thoroughly explained and NO MATH BACKGROUND IS NEEDED. This class will give you a head start among your peers.This class contains fundamentals of Image Classification with Tensorflow.This course will teach you everything you need to get started.
Short Summary about the need and importance of the CourseLinear Algebra is the backbone of Data Science, Machine Learning (ML), and Artificial Intelligence (AI). Understanding its core concepts is essential to grasp the functionality of ML algorithms. However, most courses make this process overwhelming by focusing on complex calculations rather than the practical application you need to understand the working of Machine Learning Algorithms. How our course is different ?We’ve designed this Linear Algebra course specifically for aspiring Data Scientists and Machine Learning enthusiasts who want to dive into the essentials without wasting time. In just around 7.5 hours, you’ll master the key concepts required for Machine Learning, with a clear focus on how these concepts apply directly to real-world Machine Learning algorithms. This Course will teach you the geometric intuition and essential computations so that you can think like a Machine Learning Expert.Please find the Complete Syllabus for the Course belowMathematics for Machine Learning: 1. Introduction to linear AlgebraDifference between Algebra and Linear Algebra, Definition of Linear Algebra, Linear Equation and System of linear equations with an Example, Attributes and properties of system of linear equation.Mathematics for Machine Learning: 2. Geometric representation of an expressionGeometric visualization of an algebraic expression with an example, Gradient of a straight line, Generalization of an expression geometrically on an N dimensional plane.Mathematics for Machine Learning: 3. Importance of a System of linear EquationDefinition and Goal of System of Linear Equations, General form of system of Linear Equations, representing a dataset in terms of System of linear equations, Applications of system of linear equations in solving a classification and a regression problem with an e
Welcome to the best Machine Learning and Data Science with Python course in the planet. Are you ready to start your journey to becoming a Data Scientist?In this comprehensive course, you’ll begin your journey with installation and learning the basics of Python. Once you are ready, the introduction to Machine Learning section will give you an overview of what Machine Learning is all about, covering all the nitty gritty details before landing on your very first algorithm. You'll learn a variety of supervised and unsupervised machine learning algorithms, ranging from linear regression to the famous boosting algorithms. You’ll also learn text classification using Natural Language processing where you’ll deal with an interesting problem.Data science has been recognized as one of the best jobs in the world and it’s on fire right now. Not only it has a very good earning potential, but also it facilitates the freedom to work with top companies globally. Data scientists also gets the opportunity to deal with interesting problems, while being invaluable to the organization and enjoy the satisfaction of transforming the way how businesses make decisions. Machine learning and data science is one of the fastest growing and most in demand skills globally and the demand is growing rapidly. Parallel to that, Python is the easiest and most used programming language right now and that’s the first language choice when it comes to the machine learning. So, there is no better time to learn machine learning using python than today.I designed this course keeping the beginners and those who with some programming experience in mind. You may be coming from the Finance, Marketing, Engineering, Medical or even a fresher, as long as you have the passion to learn, this course will be your first step to become a Data Scientist.I have 20 hours of best quality video contents. There are over 90 HD video lectures each rangi
A comprehensive introduction to AI for civic actors, focusing on its applications in governance, public safety, and social services, designed for civic leaders, non-profit staff, and public sector professionals.
As data scientists, we know the importance of being able to process and analyze large amounts of data quickly and accurately. However, with the explosion of data in recent years, traditional methods are becoming increasingly inadequate. That's where ChatGPT comes in.In this course, you'll learn how to use ChatGPT in data science, including how to train it on your own data and how to use it to generate new data. We'll also cover advanced techniques such as fine-tuning and transfer learning, so you can customize ChatGPT to your specific needs.Top Reasons why you should become a Data Scientist : Why data science? It is simple. Making sense of data will reduce the horrors of uncertainty for organizations. As organizations trying to meddle with petabytes of data, a data scientist’s role is to help them utilize this opportunity to find insights from this data pool.Data scientists are in constant demand because it is a data-heavy world!Be a part of the world's digital transformation.The demand for Data Science professionals is on the rise. This is one of the most sought-after profession currently.There are multiple opportunities across the Globe for everyone with this skill.Great career trajectory with data science – you will have rewarding career growth in this field.As a Data scientist, you can expect to take away a great salary package. Usual data scientists are paid great salaries, sometimes much above the normal market standards due to the critical roles and responsibilities.Competition is less, but demand is not.Top Reasons why you should choose this Course :This course is designed keeping in mind the students from all backgrounds - hence we cover everything from basics, and gradually progress towards more important topics around leveraging ChatGPT as a Data Scientist.</
In this intensive one-hour course, you’ll dive headfirst into the world of machine learning using TensorFlow and Google Colab. No pit stops—just pure acceleration!What You’ll Cover:TensorFlow Basics: Understand the core concepts, from defining layers to training models.Google Colab Mastery: Leverage Colab’s cloud-based environment for seamless development.Data Prep Express: Quickly preprocess your data without detours.Model Construction: Design and build neural networks like a seasoned pro.Training and Evaluation: Witness your model learn, iterate, and fine-tune for optimal performance.Why Take This Course?Speedy Results: Get up to speed in just one hour.Practical Skills: Apply what you learn to real-world problems.No Pit Stops: We’re all about efficiency here!Prerequisites:Basic Python knowledge (if you can write a for loop, you’re set!)Curiosity and a dash of determinationReady to accelerate your ML journey? Buckle up!Whether you’re a data enthusiast, a developer, or a curious learner, this course is your express ticket to mastering machine learning essentials. Let’s hit the road! Your course instructor is me Adam Cole, a professional software engineer with 5 years working on enterprise level applications. Feel free to send me any questions on LinkedIn at Adam Cole Adam Cole BSc MBCS.
Java Server Pages (JSP) is a server-side programming technology that enables the creation of dynamic, platform-independent method for building Web-based applications. JSP have access to the entire family of Java APIs, including the JDBC API to access enterprise databases. This tutorial will teach you how to use Java Server Pages to develop your web applications in simple and easy steps.Why to Learn JSP?JavaServer Pages often serve the same purpose as programs implemented using the Common Gateway Interface (CGI). But JSP offers several advantages in comparison with the CGI.Performance is significantly better because JSP allows embedding Dynamic Elements in HTML Pages itself instead of having separate CGI files.JSP are always compiled before they are processed by the server unlike CGI/Perl which requires the server to load an interpreter and the target script each time the page is requested.JavaServer Pages are built on top of the Java Servlets API, so like Servlets, JSP also has access to all the powerful Enterprise Java APIs, including JDBC, JNDI, EJB, JAXP, etc.JSP pages can be used in combination with servlets that handle the business logic, the model supported by Java servlet template engines.Finally, JSP is an integral part of Java EE, a complete platform for enterprise class applications. This means that JSP can play a part in the simplest applications to the most complex and demanding.AudienceThis tutorial has been prepared for the beginners to help them understand basic functionality of Java Server Pages (JSP) to develop your web applications. After completing this tutorial you will find yourself at a moderate level of expertise in using JSP from where you can take yourself to next levels.
Learn Machine Learning from scratch, this course for beginners who want to learn the fundamental of machine learning and artificial intelligence. The course includes video explanation with introductions(basics), detailed theory and graphical explanations. Some daily life projects have been solved by using Python programming. Downloadable files of ebooks and Python codes have been attached to all the sections. The lectures are appealing, fancy and fast. They take less time to walk you through the whole content. Each and every topic has been taught extensively in depth to cover all the possible areas to understand the concept in most possible easy way. It's highly recommended for the students who don’t know the fundamental of machine learning studying at college and university level.The objective of this course is to explain the Machine learning and artificial intelligence in a very simple and way to understand. I strive for simplicity and accuracy with every definition, code I publish. All the codes have been conducted through colab which is an online editor. Python remains a popular choice among numerous companies and organization. Python has a reputation as a beginner-friendly language, replacing Java as the most widely used introductory language because it handles much of the complexity for the user, allowing beginners to focus on fully grasping programming concepts rather than minute details. Below is the list of topics that have been covered:Introduction to Machine LearningSupervised, Unsupervised and Reinforcement learningTypes of machine learningPrincipal Component Analysis (PCA)Confusion matrixUnder-fitting & Over-fittingClassificationLinear RegressionNon-linear Regression</
This comprehensive course, Machine Learning & Python Data Science for Business and AI, is designed to transform you from a data novice into a proficient practitioner. Whether you're a business professional looking to leverage data driven insights, a student eager to enter the field of AI, or a developer aiming to add powerful new skills to your toolkit, this course provides a clear, practical, and project based path to mastery.I'll skip the heavy, academic theory and dive straight into the practical application of machine learning. You'll learn by doing, building a portfolio of real world projects that are immediately applicable to business and AI challenges. Our focus is on problem-solving using the most popular and powerful tools in the industry: Python, Pandas, NumPy, Scikit-learn, and Matplotlib.By the end of this course, you'll not only understand the core concepts of machine learning but also be able to implement them with confidence. You'll gain a deep understanding of how to collect, clean, and analyze data to make accurate predictions and informed decisions.Why This Course?In today’s data driven world, organizations rely on data science and AI to stay competitive. Understanding how to harness data effectively can help businesses predict trends, optimize operations, and make smarter decisions. This course is specifically tailored to bridge the gap between technical machine learning concepts and practical business applications.What You Will LearnStart with Python fundamentals and learn how to write clean, efficient code for data analysis.Learn how to process, clean, and visualize data using popular Python libraries like Pandas, NumPy, and Matplotlib to extract meaningful insights.Understand core statistical concepts that form the foundation of machine learning, including probability, distributions, and hypoth
Welcome to "Learn Hugging Face for Mastering Generative AI with LLMs". In today's AI-driven world, Hugging Face has become a central platform for working with Large Language Models (LLMs), which have revolutionized generative AI by enabling machines to generate human-like text, answer questions, and even create original content. This course is meticulously designed to give you a deep understanding of these models and how to harness their power using Hugging Face.Our journey begins with a robust introduction to LLMs, exploring their intricacies and how to manage their compute requirements, all within the Hugging Face ecosystem. From there, we dive into the world of Hugging Face, which provides an extensive collection of pre-trained models that can be applied in a wide range of innovative applications.Practical knowledge is essential, so the course transitions into a deep dive into Transformers, a key technology behind LLMs, with a special focus on Hugging Face implementations. You'll get hands-on experience with Hugging Face tools, manipulating datasets, building custom models, and mastering tokenization.Finally, we emphasize training, fine-tuning, and quantization, with models downloaded from Hugging Face. Learn how to adjust LLMs to your needs, whether for summarization or text generation. With techniques like Instruction Fine-tuning and PEFT, you'll master the art of fine-tuning models. We’ll even show you how to train a GPT-2 from scratch using Hugging Face to generate text from a custom dataset. Then finally, we will show you how to quantize your models so that they take up less memory.
Unlock the Power of Python for Data Science and VisualizationWelcome to a comprehensive Python programming course tailored by Selfcode Academy for data science and visualization enthusiasts. Whether you're a beginner or looking to expand your skill set, this course will equip you with the knowledge you need.Master the Python Basics:Start from scratch with Python fundamentals.Learn about variables, data types, and the logic behind programming.Explore conditional statements and loops.Dive into essential data structures like lists, tuples, dictionaries, and sets.Discover the world of functions, including powerful lambda functions.Get familiar with Object-Oriented Programming (OOP) concepts.Python's Role in Data Science:Transition to data science seamlessly.Manipulate dates and times using Python's datetime module.Tackle complex text patterns with regular expressions (regex).Harness the power of built-in Python functions.Embrace NumPy for efficient numerical computing.Master Pandas and its data structures, including Series and DataFrames.Acquire data cleaning skills to handle missing values and outliers.Excel at data manipulation with Pandas, including indexing, grouping, sorting, and merging.Dive into data visualization with Matplotlib to create compelling graphs.Advanced Data Science and Visualization:Uncover insights through Exploratory Data Analysis (EDA) techniques.Automate data analysis with Pandas Profiling, DABL, and Sweetviz.Perfect your data cleaning and preprocessing techniques.Craft captivating visualizations using Seaborn.
Machine Learning, BigQuery, TensorBoard, Google Cloud, TensorFlow, Deep Learning have become key industry drivers in the global job and opportunity market. This course with mix of lectures from industry experts and Ivy League academics will help engineers, MBA students and young managers learn the fundamentals of big data and data science and their applications in business scenarios. In this course you will learn1. Data Science2. Machine Learning3. BigQuery4. TensorBoard5. Google Cloud Machine Learning6. AI, Machine Learning, Deep Learning Fundamentals7. Analyzing Data8. Supervised and Unsupervised Learning9. Building a Machine Learning Model Using BigQuery 10. Building a Machine Learning Model Using GCP and Tensorboard11. Building your own model for predicting diabetes using Decision Tree
An in-depth introduction to machine learning, covering topics from linear models to deep learning. The syllabus includes on-line algorithms and support vector machines, with practical implementation in Python projects.
This course teaches the fundamentals and principal AI concepts about clustering, dimensionality reduction, reinforcement learning, and deep learning to solve real-life problems. Students will learn the basics of several machine learning topics to help solve real-life challenges, including unsupervised learning techniques such as clustering and dimensionality reduction.
This machine learning course will provide you the fundamentals of how companies like Google, Amazon, and even Udemy utilize machine learning and artificial intelligence (AI) to glean meaning and insights from massive data sets. Glassdoor and Indeed both report that the average salary for a data scientist is $120,000. This is the standard, not the exception.Data scientists are already quite desirable. It's difficult to keep them on staff in today's tight labor market. There is a severe shortage of people who possess the rare combination of scientific training, computer expertise, and analytical talents.Today's data scientists are held to the same standards as the Wall Street "quants" of the '80s and '90s. When the need arose for innovative algorithms and data approaches, physicists and mathematicians flocked to investment banks and hedge funds.So, it's no surprise that data science is rising to prominence as a promising career path in the modern day. It is analytic in focus, driven by code, and performed on a computer. As a result, it shouldn't be a shock that the demand for data scientists has been growing steadily in the workplace for the past few years.On the other hand, availability has been low. Obtaining the education and experience necessary to be hired as a data scientist is tough. And that's why we made this course in the first place!Each topic is described in plain English, and the course does its best to avoid mathematical notations and jargon. Once you have access to the source code, you can experiment with it and improve upon it. Learning and applying these algorithms in the real world, rather than in a theoretical or academic setting, is the focus of this course.Each video will leave you with a new perspective that you can implement right away!If you have no background in statistics, don't let that stop you from enrolling in this course; we welcome students of all levels.
Computer Vision Web Development course will take you from the very basics right up till you are comfortable enough in creating your own web apps. By the end of the course, you will have the skills and knowledge to develop your own computer vision applications on the web. Whether it’s Custom Object Detection or simple Color Detection you can do almost everything on the web.This comprehensive course covers a range of topics, including:Basics of Web DevelopmentBasics of Computer VisionBasics of OpenCV jsComputer Vision and Web IntegrationGraphical InterfaceVideo Processing in the Browser using OpenCV.jsObject DetectionCustom Object DetectionTensorFlow for JavaScriptDeep Learning on the WebComputer Vision AdvancedCreating 10+ CV Web AppsBuilding a Photoshop Web Application with OpenCV.jsReal-Time Face Detection in the Browser with OpenCV.js & Haar Cascade ClassifierReal-time Object Detection in the Browser using YOLOv8 and TensorFlow.jsObject Detection in Images & Videos in the Browser using YOLOv8 & TensorFlow.jsPersonal Protective Equipment (PPE) Detection in the Browser using YOLOv8 and TensorFlow.jsAmerican Sign Language (ASL) Letters Detection in the Browser using YOLOv8 and TensorFlow.jsLicence Plate Detection and Recognition in the Browser using YOLOv8 and Tesseract.js
Deep Learning is a hot topic today! This is because of the impact it's having in several industries. One of the fields in which deep learning has the most influence today is Natural Language Processing.To understand why Deep Learning based Natural Language Processing is so popular; it suffices to take a look at the different domains where giving a computer the power to understand and make sense out of text and generate text has changed our lives.Some applications of Natural Language Processing are in:Helping people around the world learn about any topic ChatGPTHelping developers code more efficiently with Github Copilot.Automatic topic recommendation in our Twitter feedsAutomatic Neural Machine Translation with Google TranslateE-commerce search engines like those of AmazonCorrection of Grammar with GrammarlyThe demand for Natural Language Processing engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn’t easy. There’s so much information out there, much of which is outdated and many times don't take the beginners into consideration :(In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step and project-based approach. You shall be using Tensorflow 2 (the world's most popular library for deep learning, built by Google) and Huggingface transformers (most popular NLP focused library ). We shall start by understanding how to build very simple models (like Linear regression model for car price prediction and RNN text classifiers for movie revi
This course has been designed by a specialist team of software developers who are passionate about using JavaScript with Machine Learning. We will guide you through complex topics in a practical way, and reinforce learning with in-depth labs and quizzes.This is the tutorial you've been looking for to become a modern JavaScript machine learning master in 2020. It doesn’t just cover the basics, by the end of the course you will have advanced machine learning knowledge you can use on you resume. From absolute zero knowledge to master - join the TensorFlow.js revolution.
Hello there,Welcome to the “Data Science and Machine Learning Fundamentals [Theory Only]” course.Theorical Course for Data Science, Machine Learning, Deep Learning to understand the logic of Data Science algorithmsMachine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning helps you stay ahead of new trends, technologies, and applications in this field.Machine learning describes systems that make predictions using a model trained on real-world data.Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models. Python, machine learning, python programming, machine learning python, python for beginners, data science. Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, fri
Unlock the fast track to machine learning mastery with our comprehensive course, "Hands-on Machine Learning in Python & ChatGPT." Dive deep into hands-on tutorials utilizing essential tools like Pandas, Numpy, Seaborn, Scikit-learn, Python, and the innovative capabilities of ChatGPT. This course is designed to guide you seamlessly through every stage of the machine learning process, ensuring a complete workflow that empowers you to tackle tasks such as data cleaning, manipulation, preprocessing, and the development of powerful supervised and unsupervised machine learning models.In this immersive learning experience, gain proficiency in crafting supervised models, including Linear Regression, Logistic Regression, Random Forests, Decision Trees, SVM, XGBoost, and KNN. Unleash the power of unsupervised models like KMeans and DBSCAN for cluster analysis. The course is strategically structured to enable you to navigate through these complex concepts swiftly, effortlessly, and with precision.Our primary objective is to equip you with the skills to build machine learning models from scratch, leveraging the combined strength of Python and ChatGPT. You will not only learn the theoretical foundations but also engage in practical exercises that solidify your understanding. By the end of the course, you'll have the expertise to measure the accuracy and performance of your machine learning models, enabling you to make informed decisions and select the best models for your specific use case.Whether you are a beginner eager to enter the world of machine learning or an experienced professional looking to enhance your skill set, this course caters to all levels of expertise. Join us on this learning journey, where efficiency meets excellence, and emerge with the confidence to tackle real-world machine learning challenges head-on. Fast-track your way to becoming a proficient machine learning practitioner with our dynamic and comprehensive course.
In today's data-driven world, the ability to harness and interpret data is not just a valuable skill but a crucial advantage. Whether you're an aspiring data scientist, a seasoned professional looking to expand your skill set, or an entrepreneur aiming to leverage data for strategic decisions, our comprehensive course on data science offers a transformative learning experience.Course OverviewOur course begins with a foundational exploration of data science, introducing you to its principles and importance in various industries. You'll delve into the distinctions between data science, data engineering, and data analysis, gaining a clear understanding of their respective roles and applications. Through real-world case studies and examples, you'll discover how data science drives innovation and impacts decision-making processes across different sectors.Essential Tools and TechnologiesTo equip you with the tools needed for effective data analysis, the course covers essential programming languages such as Python . Whether you're manipulating data with Pandas, performing numerical operations with NumPy, or creating insightful visualizations with Matplotlib and Seaborn, you'll develop a versatile skill set that forms the backbone of data science projects.Practical Skills DevelopmentA significant focus of the course is hands-on learning. You'll gain practical experience in gathering, cleaning, and analyzing data from diverse sources. You'll hone your ability to transform raw data into actionable insights that drive business decisions.Environment Setup and Best PracticesNavigating the data science environment can be daunting, especially for beginners. That's why we guide you through the setup of Python and Jupyter Notebook on both Windows and macOS, ensuring you're equipped with the right tools from the start. You'll learn to create and manage virtual environments, enhancing your ability to work efficiently and maintain project dependencies.Data
A beginner-friendly project on creating a decision tree classifier using the R programming language.
Are you interested in Artificial Intelligence (AI), Machine Learning and Artificial Neural Network?Are you afraid of getting started with Deep Learning because it sounds too technical?Have you been watching Deep Learning videos, but still don’t feel like you “get” it?I’ve been there myself! I don’t have an engineering background. I learned to code on my own. But AI still seemed completely out of reach.This course was built to save you many months of frustration trying to decipher Deep Learning. After taking this course, you’ll feel ready to tackle more advanced, cutting-edge topics in AI.In this course:We assume as little prior knowledge as possible. No engineering or computer science background required (except for basic Python knowledge). You don’t know all the math needed for Deep Learning? That’s OK. We'll go through them all together - step by step.We'll "reinvent" a deep neural network so you'll have an intimate knowledge of the underlying mechanics. This will make you feel more comfortable with Deep Learning and give you an intuitive feel for the subject.We'll also build a basic neural network from scratch in PyTorch and PyTorch Lightning and train an MNIST model for handwritten digit recognition.After taking this course:You’ll finally feel you have an “intuitive” understanding of Deep Learning and feel confident expanding your knowledge further.If you go back to the popular courses you had trouble understanding before (like Andrew Ng's courses or Jeremy Howards' Fastai course), you’ll be pleasantly surprised at how much more you can understand.You'll be able to understand
Course overviewTransform how you communicate with large language models. This intensive, hands-on program teaches the principles and practice of prompt engineering for modern LLMs (ChatGPT, GPT-4 and similar models). Learners will master a proven set of techniques to reliably shape outputs, extract high-value insights, and optimize model performance for real-world tasks.Why this coursePractical focus: workshops, real-world case studies, and iterative feedback cycles.Framework-driven: learn a repeatable prompt-design method (instruction, context, examples, persona, format, tone) to improve consistency and controlTool-ready: apply techniques across ChatGPT/GPT-4 and complementary AI tools used in industry workflowsCourse structureFoundation & TheoryModern LLM architectures and capabilities (ChatGPT, GPT-4, distinctions from GPT-3.5)Core prompt engineering principles and behavioral mechanicsContextual conversation design and session-state managementResponse quality metrics and performance boundariesPractical ApplicationsHands-on prompt-crafting labs with iterative testing and evaluationIndustry-specific use cases (marketing, product, data, support, engineering)Peer review & instructor feedback sessionsPerformance tuning and evaluation exercisesCore modules (7)Module 1 — ChatGPT & LLM EssentialsLLM architectures, strengths, and limitationsModel behavior, safety considerations, and hallucination mitigationModule 2 — Engineering FundamentalsCore prompt-building blocks and decompositionOutput-targeting techniques and common pitfallsModule 3 — Context Mastery<
Module-1Welcome to the Pre-Program Preparatory ContentSession-1:1) Introduction2) Preparatory Content Learning ExperienceMODULE-2INTRODUCTION TO PYTHONSession-1:Understanding Digital Disruption Course structure1) Introduction2) Understanding Primary Actions3) Understanding es & Important PointersSession-2:Introduction to python1) Getting Started — Installation2) Introduction to Jupyter NotebookThe Basics Data Structures in Python3) Lists4) Tuples5) Dictionaries6) SetsSession-3:Control Structures and Functions1) Introduction2) If-Elif-Else3) Loops4) Comprehensions5) Functions6) Map, Filter, and Reduce7) SummarySession-4:Practice Questions1) Practice Questions I2) Practice Questions IIModule-3Python for Data ScienceSession-1:Introduction to NumPy1) Introduction2) NumPy Basics3) Creating NumPy Arrays4) Structure and Content of Arrays5) Subset, Slice, Index and Iterate through Arrays6) Multidimensional Arrays7) Computation Times in NumPy and Standard Python Lists8) SummarySession-2:Operations on NumPy Arrays1) Introduction2) Basic Operations3) Operations on Arrays4) Basic Linear Algebra Operations5) SummarySession-3:Introduction to Pandas1) Introduction2) Pandas Basics3) Indexing and Selecting Data4) Merge and Append5) Grouping and Summarizing Data frames6) Lambda function & Pivot tables7) SummarySession-4:Getting and Cleaning Data1) Introduction2) Reading Delimited and Relational Databases3) Reading Data from Websites4) Getting Data from APIs5) Reading Data from PDF Files6) Cl
Course DescriptionThis tutorial course is a practical, project driven introduction to Machine Learning and Deep Learning using PyTorch. Each concept is taught through real world examples, allowing professionals to quickly understand, how models work and how they are used in real applications. You will build complete end to end projects such as LSTM based sentiment analysis, RNN based spam detection, CNN models for image classification, MLP networks for video quality prediction, and regression models using real datasets from sales, finance, and home loan scenarios. This tutorial course also covers how to convert Jupyter Notebook experiments into a clean, modular Python project structure suitable for production use.By combining NLP, computer vision, and predictive analytics use cases, this tutorial course helps you gain solid practical experience in PyTorch while learning how to preprocess data, design model architectures, train models, evaluate results, and prepare solutions for real-world implementation.This Tutorial Course Primarily Focuses On:Building ML & DL models end to end in PyTorchPerforming data preprocessing and feature engineeringTraining, evaluating, and deploying models with real datasetsUnderstanding architectures like LSTM, CNN, DNN, Decision Trees, Random Forest & MLPConverting research notebooks into production ready Python modulesBy the end of this course, You will be able toBuild machine learning regression & classification modelsDevelop CNN, RNN, MLP, and LSTM architectures in PyTorchPerform NLP tasks like sentiment analysis & spam detectionImplement image classification models for handwritten alphabets & traffic signsConvert notebooks into modular Python project structuresWork with real time data for prediction and quality assessmentYou will learn in this tutorial courseDec
The course Fundamentals Data Science and Machine Learning is a meticulously designed program that provides a comprehensive understanding of the theory, techniques, and practical applications of data science and machine learning. This immersive course is suitable for both beginners and experienced professionals seeking to enhance their knowledge and skills in this rapidly evolving field.Greetings, Learners! Welcome to the Data Science and Machine Learning course. My name is Usama, and I will be your instructor throughout this program. This comprehensive course consists of a total of 9 lectures, each dedicated to exploring a new and crucial topic in this field.For those of you who may not possess prior experience or background knowledge in Data Science and Machine Learning, there is no need to worry. I will commence the course by covering the fundamentals and gradually progress towards more advanced concepts.Now, let's delve into the course outline, which encompasses the following key areas:Data Science: We will dive into the interdisciplinary field of Data Science, exploring techniques and methodologies used to extract meaningful insights from data.Artificial Intelligence: This topic delves into the realm of Artificial Intelligence (AI), where we will explore the principles and applications of intelligent systems and algorithms.Deep learning: Subfield of machine learning that focuses on training artificial neural networks to learn and make predictions from complex and large-scale data. This course provides an overview of deep learning, covering key concepts, algorithms, and applications.Machine Learning: We will extensively cover Machine Learning, which forms the backbone of Data Science, enabling computers to learn and make predictions from data without being explicitly programmed.Data Engineering: This area focuses on the
Welcome to the Captivating World of LLM Prompt Engineering!This course empowers you to unlock the true potential of Large Language Models (LLMs), regardless of your experience level. Whether you're a seasoned professional or a curious beginner, this comprehensive program equips you with the skills to become a master of LLM prompt engineering.Master the Art of Crafting Powerful Prompts:Diverse Task Applications: Craft effective prompts tailored to various tasks, including generating informative summaries, creating captivating stories, or even translating languages, all through the power of well-designed prompts.Advanced Techniques Exploration: Move beyond the basics and delve into advanced concepts like iterative prompting, where you refine your prompt based on the LLM's initial output. Additionally, explore few-shot learning, allowing you to achieve impressive results even with limited data.Core LLM Concepts Demystified: Gain a solid understanding of fundamental LLM properties like statelessness and quantization. Explore how these properties impact prompt design and LLM behavior. Learn to identify and mitigate potential hallucinations in LLM outputs.Unleash LLM Capabilities Through Hands-on Learning:Code Walkthroughs Deepen Understanding: Go beyond theory with interactive code walkthroughs using Lamma 2 as a platform. Actively explore code examples to gain practical experience in setting up, configuring LLMs, working with advanced models (e.g., quantized models), and leveraging specialized notebooks like AWQ for optimized workflows.Real-World Applications Solidify Skills: This course emphasizes the practical application of LLM prompt engineering. Learn how to tailor prompts to solve specific real-world problems, ensuring accurate and creative AI outputs. Translate your newfound knowledg
In today's data-driven world, the ability to harness and interpret data is not just a valuable skill but a crucial advantage. Whether you're an aspiring data scientist, a seasoned professional looking to expand your skill set, or an entrepreneur aiming to leverage data for strategic decisions, our comprehensive course on data science offers a transformative learning experience.Course OverviewOur course begins with a foundational exploration of data science, introducing you to its principles and importance in various industries. You'll delve into the distinctions between data science, data engineering, and data analysis, gaining a clear understanding of their respective roles and applications. Through real-world case studies and examples, you'll discover how data science drives innovation and impacts decision-making processes across different sectors.Essential Tools and TechnologiesTo equip you with the tools needed for effective data analysis, the course covers essential programming languages such as Python and R. Whether you're manipulating data with Pandas, performing numerical operations with NumPy, or creating insightful visualizations with Matplotlib and Seaborn, you'll develop a versatile skill set that forms the backbone of data science projects.Practical Skills DevelopmentA significant focus of the course is hands-on learning. From introductory SQL for data querying to advanced techniques in web scraping for data retrieval, you'll gain practical experience in gathering, cleaning, and analyzing data from diverse sources. Through interactive exercises and projects, you'll hone your ability to transform raw data into actionable insights that drive business decisions.Environment Setup and Best PracticesNavigating the data science environment can be daunting, especially for beginners. That's why we guide you through the setup of Python and Jupyter Notebook on both Windows and macOS, ensuring you're equipped with the right tools from the start. You'll l
A guided project on Coursera that provides a hands-on introduction to essential causal inference techniques for data science.
This course provides an introduction to robotics, covering the core techniques for representing robots that perform physical tasks in the real world.
< Step-by-step explanation of more than 7 hours of video lessons on Supervised Machine Learning: Complete Masterclass [2023]><Instant reply to your questions asked during lessons><Weekly live talks on Supervised Machine Learning: Complete Masterclass [2023]. You can raise your questions in a live session as well><Helping materials like notes, examples, and exercises><Solution of quizzes and assignments> Welcome to the Machine Learning course!In this comprehensive course, you will learn the fundamental concepts and techniques used in Machine Learning. We will cover a range of topics from data preprocessing to model evaluation and selection, with hands-on exercises and projects to help you build and solidify your understanding of the concepts.The course is designed for beginners, but it will also be valuable for those who have some experience in programming and data analysis. You will be guided through the basics of Python programming and the most commonly used libraries for data manipulation and visualization, such as Pandas and Matplotlib.Once you have mastered the basics, we will delve into the core concepts of Machine Learning, including supervised and unsupervised learning, decision trees, random forests, clustering, neural networks, and deep learning. You will learn how to preprocess data, train and evaluate models, and optimize them for better performance.In addition to the theory, you will also have hands-on practice using real-world datasets and implementing Machine Learning algorithms with Python. By the end of the course, you will be able to apply Machine Learning techniques to solve a wide range of problems and use cases, and have the skills to further your studies in this exciting and rapidly growing field.Whether you are a student, a researcher, or a professional
Unlock the power of interactive data science with Interactive Data Science in Python — a comprehensive, beginner-friendly course designed to take you from novice to confident practitioner. We begin by exploring Shiny, the dynamic and popular web app framework for Python, where you'll learn how to build interactive dashboards, responsive data visualizations, and user-friendly interfaces using the classic Shiny library. Once you’ve gained solid skills, you’ll transition smoothly to Shiny Express, a modern, more streamlined toolkit that accelerates app development while maintaining full flexibility.Alongside Shiny, you’ll dive deep into essential Python data science libraries like Pandas, Seaborn, and Matplotlib. You’ll master how to clean, analyze, visualize, and explore complex datasets with clarity and precision, empowering you to uncover patterns and tell compelling stories with data.This course also introduces PyTorch basics from scratch — perfect for beginners eager to explore deep learning and neural networks. You’ll grasp fundamental machine learning concepts and get hands-on experience building your own models, preparing you to confidently tackle more advanced AI projects.Throughout the course, you’ll engage with practical coding exercises, real-world datasets, and projects focused on creating interactive applications that captivate users and dynamically reveal insights. Whether you aspire to be a data scientist, analyst, or developer, this course will equip you with the skills and confidence to build powerful data-driven applications and understand foundational deep learning techniques in Python.Jump in today and bring your data to life with interactive, intelligent applications!
Learn Python for Data Analysis and Visualization
This course provides an introduction to AI image generation using Stable Diffusion, covering denoising techniques and advanced generative learning methods like autoencoders and contrastive learning.
Welcome to our comprehensive course on Deep Learning with R! This course is designed to provide you with a thorough understanding of deep learning principles and their practical implementation using the R programming language.In this course, you will embark on a journey into the fascinating world of neural networks and heuristics, gaining the skills and knowledge necessary to leverage the power of deep learning for various applications. Whether you're a beginner or an experienced data scientist, this course offers something for everyone.Section 1: Deep Learning: Neural Networks With RIn the first section, you will dive into the fundamentals of deep learning using neural networks. Starting with dataset review and dataframe creation, you'll learn how to manipulate data effectively for analysis. Through practical exercises, you'll gain hands-on experience in running neural network code and generating outputs from datasets. By the end of this section, you'll be equipped with the foundational skills needed to build and train neural networks using R.Section 2: Deep Learning: Heuristics Using RIn the second section, you'll explore advanced techniques in deep learning, focusing on the application of heuristics using R. From descriptive statistics generation to linear regression modeling, you'll learn how to analyze datasets related to cryptocurrencies, energy sectors, and financial markets. Through a series of practical examples, you'll master the art of data manipulation and visualization, gaining insights into complex relationships between variables.By the end of this course, you'll have a solid understanding of deep learning principles and the ability to apply them confidently in real-world scenarios using R. Whether you're interested in predictive modeling, pattern recognition, or data analysis, this course will empower you to unlock the full potential of deep learning with R. Let's dive in and explore the exciting world of neural networks
Unlock the power of Generative AI, ChatGPT, and Prompt Engineering to work smarter, faster, and more creatively. This hands-on course teaches you how to use today’s most effective AI tools and automation techniques to improve productivity across writing, design, video creation, voice, data analysis, and web-related tasks.You will learn practical prompt engineering techniques and how to apply them correctly with industry-leading AI platforms. Instead of focusing on theory, this course emphasizes real-world use cases, workflows, and automation strategies that professionals, creators, and businesses use every day.Throughout the course, you will work with tools such as ChatGPT, Canva Magic Studio, Runway ML, ElevenLabs, Notion AI, DALL·E 3, and other modern generative AI platforms. You will understand not only how these tools work, but how to choose the right tool, design effective prompts, and integrate AI into your daily workflow.What Makes This Course DifferentClear and practical prompt engineering explained in a simple, beginner-friendly wayHands-on, project-based lessons focused on real productivity gainsCoverage of multiple AI categories in one complete coursePractical automation and workflow strategies for work and businessTool comparisons and best practices to help you get better results fasterWho This Course Is ForThis course is designed for:Professionals who want to save time and increase efficiency using AIContent creators and marketers looking to scale output with AI toolsEntrepreneurs and freelancers aiming to use AI for business growthBeginners who want a clear and practical introduction to Generative AINo prior AI experience is required. Everything is taught step by step using real examples and prac
Learn Python for Data Science and Machine Learning Bootcamp
This course is an exciting hands-on view of the fundamentals of Data Science and Machine LearningData Science and Machine Learning are developing on a massive scale. Everywhere you look in society, the world wide web, or in technology, you will find Data Science and Machine Learning algorithms working behind the scenes to analyze and optimize all aspects of our lives, businesses, and our society. Data Science and Machine Learning with Artificial Intelligence are some of the hottest and fastest-developing areas right now. This course will teach you the fundamentals of Data Science and Machine Learning. This course has exclusive content that will teach you many new things regardless of if you are a beginner or an experienced Data Scientist, and aspires to be one of the best Udemy courses in terms of education and value. You will learn aboutRegression and Prediction with Machine Learning models using supervised learning. This course has the most complete and fundamental master-level regression analysis content packages on Udemy, with hands-on, useful practical theory, and automatic Machine Learning algorithms for model building, feature selection, and artificial intelligence. You will learn about models ranging from linear regression models to advanced multivariate polynomial regression models.Classification with Machine Learning models using supervised learning. You will learn about the classification process, classification theory, and visualizations as well as some useful classifier models, including the very powerful Random Forest Classifier Ensembles and Voting Classifier Ensembles.Cluster Analysis with Machine Learning models using unsupervised learning. In this part of the course, you will learn about unsupervised learning, cluster theory, artificial intelligence, explorative data analysis, and seven useful Machine Learning clustering algorithms ranging from hierarchical cl
No Prior Experience Needed – Learn with Real Projects!Are you curious about Data Science & Machine Learning but don’t know where to start? This beginner-friendly bootcamp is your perfect first step! We’ll guide you from absolute zero to building real-world projects—no math or coding background required!What You’ll Learn:Python for Beginners – Learn from scratch with easy-to-follow examples Data Science Essentials – Pandas, NumPy, and data visualization (Matplotlib & Seaborn) Machine Learning Made Simple – Predict trends, classify data & uncover patternsHands-On Projects – Work with real datasets (sales predictions, customer behavior, and more!)AI & ChatGPT Basics – Get introduced to cutting-edge tools like LLMs (Large Language Models) Why This Course?Perfect for Beginners – Starts slow, explains every step, and builds confidenceLearn by Doing – No boring theory—just fun, practical projects you can showcaseNo Experience Needed – We teach Python & math basics along the way Supportive Community – Get help whenever you’re stuck Certificate of Completion – Boost your resume with a valuable skill Who Is This For? Total beginners who want to explore Data Science & AI Students & professionals looking for a high-income skill Career changers curious about tech jobs Anyone who wants to future-proof their skills in 2025! Tools You’ll Use (No Setup Hassle!): Python (easy-to-learn) Jupyter Notebooks (user-friendly coding) Scikit-Learn (simple ML models) ChatGPT & AI tools (see how they work!) Bonus: Downloadable exercises & solutions Cheat sheets & study guides Lifetime access & updates Start Your Data Science Journey Today – No Experience Needed!
Ace the NVIDIA-Certified Associate Generative AI LLMs (NCA-GENL) Exam: Your Comprehensive Preparation GuideAre you ready to dive into the exciting world of Generative AI and Large Language Models (LLMs)? Do you aspire to become a certified expert in this rapidly evolving field? Look no further! Our meticulously designed practice test course is your ultimate companion for conquering the NVIDIA-Certified Associate Generative AI LLMs (NCA-GENL) exam.What is the NCA-GENL Certification?The NCA-GENL certification is a prestigious credential offered by NVIDIA, a global leader in AI and accelerated computing. It validates your foundational knowledge and skills in developing, deploying, and maintaining AI-driven applications that leverage the power of generative AI and LLMs. Achieving this certification demonstrates your expertise and commitment to staying at the forefront of AI innovation.Why Choose Our Practice Test Course?Realistic Exam Simulations: We understand that practice makes perfect. Our course features multiple practice exams that mirror the format, difficulty level, and time constraints of the actual NCA-GENL exam. By immersing yourself in these realistic simulations, you'll gain the confidence and familiarity needed to excel on test day.Comprehensive Coverage: We leave no stone unturned in our quest to prepare you for success. Our practice tests cover all the essential topics outlined in the NCA-GENL exam blueprint, including:Fundamentals of Generative AI and LLMs: Deep dive into the core concepts, architectures, and applications of generative AI and LLMs.Transformer Models and Architectures: Gain a thorough understanding of transformer models, the backbone of many state-of-the-art LLM architectures.Prompt Engineering: Learn the art of crafting effective prompts to el
Welcome to the 10 Days of Prompt Engineering, Generative AI, and Data Science CourseGet hands-on with Prompt Engineering, Generative AI, and Data Science in just 10 days. I’m Diogo, and I’ve structured this course to take you from basics to advanced topics quickly. We’ll cover live sessions, hands-on labs, and real-world projects—all in 14 hours and 30 minutes of published video content. You’ll also receive lifetime updates so your learning never goes stale.You will build a portfolio of project on topics like:Prompt Engineering Fundamentals: Understand transformers, attention mechanisms, and how to structure prompts for optimal performance.Generative AI Workflows: Master tools like Google Colab, Jupyter Notebook, LM Studio, and learn how to fine-tune system messages and model parameters.OpenAI API for Text & Images: Integrate the OpenAI API into Python projects, explore parameters for better text generation, and tap into image generation (coming soon).Machine Learning with XGBoost & Random Forest: Explore advanced ML topics, including parameter tuning, SHAP values, and real-world approaches to customer satisfaction modeling.AI Agents with CrewAI: Dive into the next wave of AI automation (coming in Q1 2025).COURSE BREAKDOWNIntroductionMeet your instructor, download course materials, set up your environment (Google Colab, Jupyter Notebook, RStudio).Preview the core projects we’ll tackle.Day 1 – Basics of Prompt EngineeringLearn about transformers, attention, and chain-of-thought prompting.Experiment with LM Studio to practice
Welcome to this course, OpenAI Mastery: Prompt Engineering, ChatGPT, Dall-e, API etcDive into the world of AI with our all-inclusive course bundle, expertly designed to guide you through the intricate landscape of OpenAI’s cutting-edge technologies. Whether you're a complete beginner or looking to sharpen your skills, this collection of courses is your gateway to mastering AI.What You Will Learn:OpenAI Essentials: Grasp the fundamentals of OpenAI, including prompt engineering, and gain the know-how to create your own OpenAI account. You'll understand the core business concepts that will set you apart in the AI industry.ChatGPT Mastery with Free Version: Navigate the functionalities of ChatGPT without a cost barrier. Learn to personalize settings, engineer prompts effectively, and explore the distinctions between the free and plus versions.Dall-e Mastery with AI Art Generation: Unleash your creative potential by generating stunning AI art. Get hands-on experience with Dall-e functions, understand the mechanics of prompts, and delve into the nuances of UI, inpainting, and outpainting techniques.OpenAI API Mastery: Play with Models to unlock the power of OpenAI APIs. Build your own ChatGPT, sentiment analysis tools, image generation models, and even audio-to-text applications.ChatGPT Plus Mastery with Custom GPTs: Go beyond the basics with custom GPT models. Learn to tailor ChatGPT to your needs, understand its API capabilities, and grasp the comprehensive concepts of API & Actions.Course Features:Step-by-step instructions on each topic.Real-world applications and examples.Access to a community of AI enthusiasts.Continuously updated content reflecting the latest AI trends.Enroll now to begin your journey i
Welcome to this course on Data Science and Machine Learning with Microsoft Azure. You would learn various lessons for Data Visualization, Data Cleaning and Data Analysis using Microsoft Power BI. It is a powerful Business Intelligence software that can be used for various domains ranging from creating Analytics dashboard and Business Intelligence reports to fetching information from wide range of data sources. You could also perform various types of data cleaning operations using Power Query. Moreover, if you want to create some advanced types of Analytics charts you can write a few lines of code in python using frameworks such as Matplotlib and Seaborn. And if you want to modify the dataset either by creating derived values based on certain mathematical formula or specified conditions you could perform various Data Modelling operations as well by using creating Calculated fields and by using Power Query editor. In this course you would learn various such concepts with completely practical examples on Power BI Desktop, that can be applied in the similar way on azure cloud.After you have learned various lessons on Power BI, you would be learning Azure Machine learning in the later sections of this course. Here you would learn to analyze an image using Computer Vision. And you would also learn to perform language detection, sentiment analysis, key phrase extraction and entity recognition using Azure Text Analytics. Here in this course you will learn following lessons on Data Science using Microsoft Power BI-Creating Visualization charts such as Bar chartPie chartDonut or Ring chartTreemap chartInteractive charts and Drill downTable and MatrixDate and other SlicersCreating a calculated fieldGauge chartMap chart and modesScatterplot and Animation PlaybackBasics of Power QueryRow deletion and
One of the most essential aspects of Data Science or Machine Learning is Data Cleaning. In order to get the most out of the data, your data must be clean as uncleaned data can make it harder for you to train ML models. In regard to ML & Data Science, data cleaning generally filters & modifies your data making it easier for you to explore, understand and model.A good statistician or a researcher must spend at least 90% of his/her time on collecting or cleaning data for developing a hypothesis and remaining 10% on the actual manipulation of the data for analyzing or deriving the results. Despite these facts, data cleaning is not commonly discussed or taught in detail in most of the data science or ML courses. With the rise of big data & ML, now data cleaning has also become equally important.Why should you learn Data Cleaning?Improve decision makingImprove the efficiencyIncrease productivityRemove the errors and inconsistencies from the datasetIdentifying missing valuesRemove duplicationWhy should you take this course?Data Cleaning is an essential part of Data Science & AI, and it has become an equally important skill for a programmer. It’s true that you will find hundreds of online tutorials on Data Science and Artificial Intelligence but only a few of them cover data cleaning or just give the basic overview. This online guide for data cleaning includes numerous sections having over 5 hours of video which are enough to teach anyone about all its concepts from the very beginning. Enroll in this course now to learn all the concepts of Data Cleaning. This course teaches you everything including the basics of Data Cleaning, Data Reading, merging or splitting datasets, different visualization tools, locate or handling missing/absurd values and hands-on sessions whe
This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning and also the basics of Machine learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand and its application . Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This is a comprehensive course with very crisp and straight forward intent. This course covers a variety of topics, including Neural Network BasicsTensorFlow detailed,Keras,Sonnet etcArtificial Neural NetworksTypes of Neural networkFeed forward networkRadial basis networkKohonen Self organizing mapsRecurrent neural NetworkModular Neural networksDensely Connected NetworksConvolutional Neural NetworksRecurrent Neural NetworksMachine Learning Deep Learning Framework comparisons There are many Deep Learning Frameworks out there, so why use TensorFlow? TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the grap
Learn Python for Machine Learning & Data Science Masterclass
Hi all Its Jay I am a data scientist by profession and Instructor by passion I have around 4 years of experience as data scientist, I started my career as analyst as gradually moved to data scientist hence I can understand what are programming prerequisites for data scientist. This course is created for absolute beginners of data science and machine learning. It covers all aspect of python languages required in data science machine learning and deep learning.
Artificial Intelligence is transforming the world — and AI agents are at the heart of that revolution. From virtual assistants and research bots to automated problem-solvers, agents are the next leap beyond simple chatbots. In this hands-on course, you’ll learn step-by-step how to build your own working AI agent using LangChain, Python, and the OpenAI API — no advanced technical experience required.We’ll start from the very beginning. You’ll understand what AI really means, how agents differ from chatbots, and where they’re being used in the real world. Then, you’ll dive into the practical side — setting up your Python environment, installing key tools, and connecting APIs like OpenAI and SerpAPI to bring intelligence and search power to your creations.Once your setup is complete, we’ll explore the core concepts of LangChain — including chains, agents, prompts, and tools. You’ll learn how to create prompt templates, build your own tools, and finally, connect them all to create an AI agent that can think, search, and respond intelligently. By the end, you’ll not only understand how LangChain works, but also how to build, test, and customize your own AI-powered assistants for real-world use.Whether you’re an aspiring developer, a curious beginner, or an entrepreneur exploring the future of automation, this course gives you the practical foundation you need to start building with confidence.By completing this course, you’ll be able to:Understand the fundamentals of AI and LangChainSet up your development environment from scratchBuild and test working AI agents using Python and OpenAIExtend your agents with real-world tools and APIsJoin today, and take your first step toward building intelligent AI agents that can reason, search, and act — all powered by LangChain.
Deep learning has solved tons of interesting real-world problems in recent years. Apache Spark has emerged as the most important and promising Machine Learning tool and currently a stronger challenger of the Hadoop ecosystem. In this course, you’ll learn about the major branches of AI and get familiar with several core models of Deep Learning in its natural way. This comprehensive 3-in-1 course is a fast-paced guide to implementing practical hands-on examples, streamlining Deep Learning with Apache Spark. You’ll begin by exploring Deep Learning Neural Networks using some of the most popular industrial Deep Learning frameworks. You’ll apply built-in Machine Learning libraries within Spark, also explore libraries that are compatible with TensorFlow and Keras. Next, you’ll create a deep network with multiple layers to perform computer vision and improve cybersecurity with Deep Reinforcement Learning. Finally, you’ll use a generative adversarial network for training and create highly distributed algorithms using Spark.By the end of this course, you'll develop fast, efficient distributed Deep Learning models with Apache Spark.Contents and OverviewThis training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Deep Learning with Apache Spark, covers deploying efficient deep learning models with Apache Spark. The tutorial begins by explaining the fundamentals of Apache Spark and deep learning. You will set up a Spark environment to perform deep learning and learn about the different types of neural net and the principles of distributed modeling (model- and data-parallelism, and more). You will then implement deep learning models (such as CNN, RNN, LTSMs) on Spark, acquire hands-on experience of what it takes, and get a general feeling for the complexity we are dealing with. You will also see how you can use libraries such as Deeplearning4j to perform deep learning on a distributed C
Are you an HR professional or aspiring HR manager eager to master ChatGPT and AI for Human Resource Management activities?Are you looking to gain a future-ready skill that can help you automate workflows, improve employee engagement, and drive HR innovation?If yes, this course is tailored just for you!Why Take This Course?In today’s digital era, AI is rapidly transforming how Human Resources functions. From recruitment to onboarding, performance management to HR analytics — AI is at the heart of NextGen HR practices.This course, “Hands-on Generative AI for HR Professionals | ChatGPT | AI,” offers a comprehensive, beginner-friendly, and hands-on learning experience to help you unlock the true potential of ChatGPT and AI tools in your HR role.Latest Curriculum updated as of September 2025.What You Will Learn – Course Curriculum Overview:Section 1: Introduction to NLP and ChatGPT ArchitectureIntroduction to Natural Language Processing (NLP)Practical NLP activity and understanding ChatGPT architectureSection 2: Getting Started with ChatGPT for HR ProfessionalsBasics of ChatGPTReal-world use cases you’ve never heard beforeCustom GPTs for HRChatGPT integrations & APIsIntroduction to Prompt EngineeringSection 3: ChatBot & API Integration for Automating HR ActivitiesStep-by-step setup of ChatGPT chatbots for HR functionsAutomating tasks using chatbot and website integrationsSection 4: Access to The NextGen HR Reporter – Monthly NewsletterDownload HRM newsletters from March to July 2025Stay updated on global HR tech trends and use casesSection 5: Uses
This comprehensive course is your one-stop guide to learn Python Basics, Popular Data Manipulation Libraries, Deep Learning Fundamentals, Popular Generative AI Models, Large Language Models and Agentic AI frameworks, all in one place. Whether you're a beginner exploring the world of AI or a developer looking to level up, this course takes you from the ground up and beyond.We begin with Python fundamentals and dive into essential data libraries like NumPy, Pandas, and Matplotlib for effective data handling and visualization. Then, we advance into Deep Learning, building and training neural networksMode to understand the core mechanics behind AI.Generative AI is a subset of Deep Learning. Without a solid understanding of Deep Learning fundamentals, learning Generative AI becomes difficult and often confusing. That’s why I’ve combined the most essential parts from one of my previous Deep Learning courses into this course. This ensures that you build a strong foundation before diving into advanced Generative AI topics.Once the Deep Learning Fundamentals is complete, You’ll then explore the rapidly evolving field of Generative AI:From training your own GANs and VAEs, to working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Diffusion Models, this course offers hands-on projects and intuitive explanations.Finally, we introduce you to the next frontier: Agentic AI. Learn about intelligent agent architectures such as MCP, ACP, and A2A, and use cutting-edge frameworks like LangChain to build autonomous, goal-driven AI agents.What You’ll LearnPython programming basics and data manipulation using NumPy and PandasData visualization using MatplotlibFundamentals of Deep Learning and neural network trainingBuilding Generative AI models: GANs, VAEs, LLMs, and Diffusion ModelsImplementing Retrieval-Augmented Genera
¡Bienvenido al apasionante mundo de la Ciencia de Datos y Machine Learning en R! En este curso, te embarcarás en un viaje transformador para descubrir el poder de los datos y cómo convertirlos en conocimiento significativo. Aprenderás a dominar las herramientas y técnicas más avanzadas de R para analizar, visualizar y manipular datos caóticos. Además, desbloquearás el potencial de la inteligencia artificial al desarrollar modelos de aprendizaje automático capaces de predecir tendencias, clasificar información y comprender el lenguaje humano. ¡Prepárate para convertirte en un experto en la ciencia detrás de los datos y llevar tu capacidad analítica a un nivel completamente nuevo! ¿Listo para desafiar tus límites y cambiar el juego con la ciencia de datos y el aprendizaje automático en R? ¡Únete a nosotros y comienza tu emocionante aventura hacia el futuro de la tecnología y la innovación! Lo mas importante de este curso es que haremos un proyecto real para que puedas tener conocimientos adecuados y útiles en tu vida profesional. Cada que repliques este curso que realizaremos acá, iras aumentando tu probabilidad de tener {éxito en esta área. Es fundamental que tengas toda la disposición de retarte a entender este apasionante mundo. No olvides que cualquier duda puedes contactarme para que nada obstaculice tu aprendizaje
Are you ready to master Deep Learning skills?Deep Learning is a technology using which we can solve highly computational problems such as Image Processing, Image Classification, Image Segmentation, Image tagging, sound classification, video analysis, etc.Deep Learning is becoming a buzzword these days, and If you want to learn Deep Learning then It is very important for you that you should have a proper plan regarding that.Before Learning Deep Learning you must have learned Machine Learning and must possess good knowledge of the Python programming language.If you want to build super-powerful applications in Deep Learning. Then, you are at the right place.This course will provide you with in-depth knowledge on a very hot topic i.e., Deep Learning.The purpose of this course is to provide you with knowledge of key aspects of Deep Learning without any intimidating mathematics and in a practical, easy, and fun way. The course provides students with practical hands-on experience using real-world datasets.This course will cover the following topics:-1. Deep Learning (DL).2. Artificial Neural Network (ANN).3. Convolutional Neural Network (CNN).4. Recurrent Neural Network. (RCN)5. Learn to Implement the LSTMs.This course will take you through the basics to an advanced level in all the mentioned four topics.After taking this course, you will be confident enough to work independently on any projects on these topics.There are lots and lots of exercises for you to practice In this Deep Learning Course and also a 5 Bonus Deep Learning Project "Stock Market Prediction", "Fruits Identification System", "Face Expression Recognizer", "Detecting Pneumonia from Chest X-rays", and "Optimizing Crop Production".In this Optimizing Crop Production, you will learn about Precision Farming using Data Science T
This course is for everyday people looking for an intuitive, beginner-friendly introduction to the world of machine learning and data science.Build confidence with guided, step-by-step demos, and learn foundational skills from the ground up. Instead of memorizing complex math or learning a new coding language, we'll break down and explore machine learning techniques to help you understand exactly how and why they work.Follow along with simple, visual examples and interact with user-friendly, Excel-based models to learn topics like linear and logistic regression, decision trees, KNN, naïve bayes, hierarchical clustering, sentiment analysis, and more – without writing a SINGLE LINE of code.This course combines 4 best-selling courses from Maven Analytics into a single masterclass:PART 1: Univariate & Multivariate ProfilingPART 2: Classification ModelingPART 3: Regression & ForecastingPART 4: Unsupervised LearningPART 1: Univariate & Multivariate ProfilingIn Part 1 we’ll introduce the machine learning workflow and common techniques for cleaning and preparing raw data for analysis. We’ll explore univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin & box plots, scatter plots, and correlation:Section 1: Machine Learning Intro & LandscapeMachine learning process, definition, and landscapeSection 2: Preliminary Data QAVariable types, empty values, range & count calculations, left/right
The fields of Artificial Intelligence and Machine Learning are considered the most relevant areas in Information Technology. They are responsible for using intelligent algorithms to build software and hardware that simulate human capabilities. The job market for Machine Learning is on the rise in various parts of the world, and the trend is for professionals in this field to be in even higher demand. In fact, some studies suggest that knowledge in this area will soon become a prerequisite for IT professionals.To guide you into this field, this course provides both theoretical and practical insights into the latest Artificial Intelligence techniques. This course is considered comprehensive because it covers everything from the basics to the most advanced techniques. By the end, you will have all the necessary tools to develop Artificial Intelligence solutions applicable to everyday business problems. The content is divided into seven parts: search algorithms, optimization algorithms, fuzzy logic, machine learning, neural networks and deep learning, natural language processing, and computer vision. You will learn the basic intuition of each of these topics and implement practical examples step by step. Below are some of the projects/topics that will be covered:Finding optimal routes on city maps using greedy search and A* (star) search algorithmsSelection of the cheapest airline tickets and profit maximization using the following algorithms: hill climb, simulated annealing, and genetic algorithmsPrediction of the tip you would give to a restaurant using fuzzy logicClassification using algorithms such as Naïve Bayes, decision trees, rules, k-NN, logistic regression, and neural networksPrediction of house prices using linear regressionClustering bank data using k-means algorithmGeneration of association rules with A
Course Description:Unlock the full potential of AI with this comprehensive bootcamp that dives into prompt engineering, ChatGPT, and beyond! This course is designed to equip you with the knowledge and skills to leverage the power of generative AI across content creation, data analysis, programming, and AI-driven art. Whether you're a beginner or an experienced developer, this course offers a structured path to mastering AI tools, with a special focus on ChatGPT, MidJourney, Python, and other powerful AI models.What You’ll Learn:Foundational Skills in ChatGPT & Prompt Engineering: Start with a solid understanding of ChatGPT, learning basic and advanced prompt techniques, scalable prompt structures, and a prompt repository to make your prompts more powerful and reusable.Explore AI Models Beyond ChatGPT: Discover Bing AI, Google Gemini, Claude, and open-source models. Each section introduces you to a new AI tool, showcasing unique capabilities, practical applications, and how to integrate them into your workflow.Master MidJourney for AI Art: Understand MidJourney’s basics, advanced parameters, and prompt crafting techniques. Learn how to create stunning AI art, market your work on Etsy, and even replace stock images—ideal for creative professionals.Real-World Projects & Applications: Apply what you learn with hands-on projects, including content creation for ebooks, blogging, startup ideation, email marketing, and social media campaigns. You'll also dive into data analysis projects and create unique art-based products, such as coloring books, for online sales.Python Fundamentals & Advanced Topics: Gain proficiency in Python with a guided progression from core programming concepts to advanced techniques. Learn to manage data, control flows, build GUI desktop apps, and handle files efficiently—a
Machine Learning Para Todos: Fundamentos Básicos de la IA¿Sientes curiosidad por la Inteligencia Artificial pero te parece un mundo complejo? Este curso te desmitifica el Machine Learning, brindándote una base sólida y accesible, ¡sin necesidad de experiencia previa en programación o matemáticas avanzadas!"Machine Learning Para Todos" está diseñado para cualquier persona con curiosidad por la IA y el deseo de comprender cómo funciona el aprendizaje automático. No se requieren conocimientos previos especializados; solo una mente abierta y ganas de aprender. Ya seas un profesional buscando nuevas habilidades, un estudiante explorando campos emergentes o simplemente alguien interesado en la tecnología del futuro, este curso te proporcionará una base sólida para comprender y aplicar los fundamentos del Machine Learning.A través de explicaciones claras, ejemplos prácticos y ejercicios sencillos, descubrirás los conceptos fundamentales detrás de la IA que está transformando nuestro mundo. Aprenderás qué es el Machine Learning, cómo funciona, los diferentes tipos de algoritmos (como regresión y clasificación), y cómo se aplican en situaciones reales, desde recomendaciones personalizadas hasta detección de fraudes.Al finalizar este curso, tendrás una comprensión clara de los conceptos fundamentales del Machine Learning, la capacidad de identificar problemas que pueden resolverse con estas técnicas y el conocimiento básico para seguir explorando este campo en temas como Deep Learning o Redes Neuronales Profundas, Inteligencia Artificial Generativa y Agentes IA. ¡Únete a nosotros y desbloquea el potencial de la Inteligencia Artificial!
Unlock the creative potential of Generative Adversarial Networks (GANs) and Neural Style Transfer in this hands-on course, designed to guide you through the most advanced techniques in AI-driven image generation and art creation. Using TensorFlow, we will dive into the core concepts of GANs and explore their various architectures, providing you with practical skills to implement them from scratch.In the first half of the course, you'll master GANs by implementing several popular architectures:Vanilla GAN: Understand the basics of GANs and how the generator and discriminator interact.DCGAN (Deep Convolutional GAN): Learn how to generate high-quality images using convolutional layers.Wasserstein GAN (WGAN): Discover how WGAN improves stability and reduces mode collapse in GAN training.Conditional GAN (CGAN): Create conditional models that allow for more control over generated images.Pix2Pix GAN: Learn how to convert images from one domain to another, such as turning sketches into photos.Cycle GAN: Master the art of unpaired image-to-image translation, perfect for tasks like photo enhancement or style transfer.In the second part of the course, we delve into the fascinating world of Neural Style Transfer:Vanilla Neural Style Transfer: Learn how to blend the content of one image with the style of another.Feed Forward Style Transfer: Understand the advantages of using fast neural networks for style transfer.Arbitrary Style Transfer: Generate any artistic style on any content image, enabling limitless creativity.GauGAN: Create realistic images using a simple sketch, by applying a powerful
Hi there,Welcome to "Generative AI for Data Analysis and Engineering with ChatGPT" course.ChatGPT and AI | Data Analytics and ML Mastering Course with ChatGPT-4o and Next-Gen AI Techniques for Data AnalystArtificial Intelligence (AI) is transforming the way we interact with technology, and mastering AI tools has become essential for anyone looking to stay ahead in the digital age. In today's data-driven world, the ability to analyze data, draw meaningful insights, and apply machine learning algorithms is more crucial than ever. This course is designed to guide you through every step of that journey, from the basics of Exploratory Data Analysis (EDA) to mastering advanced machine learning algorithms, all while leveraging the power of ChatGPT-4o.Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information about whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat.A machine learning course teaches you the technology and concepts behind predictive text, virtual assistants, and artificial intelligence. You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover
Are you ready to start your journey into Python programming and machine learning? This course is your ultimate guide to becoming a skilled Python programmer and mastering machine learning from scratch. Whether you're a beginner or have some experience, this course will take you from zero to hero with a practical, hands-on approach.What You’ll Learn:Python Fundamentals: Master variables, data types, control flow, functions, and libraries.Data Preprocessing: Learn to clean, scale, and transform data for machine learning models.Machine Learning Basics: Build regression and classification models with real-world datasets.Advanced ML Techniques: Explore clustering, dimensionality reduction, and ensemble learning.Real-World Projects: Solve practical problems like predicting housing prices and customer segmentation.Why Take This Course?This course is designed for learners who want to gain practical programming and machine learning skills. You’ll work on real-world projects, gaining confidence to apply these skills in various industries. By the end of the course, you’ll have a strong portfolio and the ability to build your own machine learning models.Who This Course is For:Complete beginners looking to learn Python and machine learning.Professionals aiming to enhance their data science skills.Students and developers curious about applying machine learning in real-world scenarios.Join now to kickstart your career in data science and AI!
From Google Translate to Netflix recommendations, neural networks are increasingly being used in our everyday lives. One day neural networks may operate self driving cars or even reach the level of artificial consciousness. As the machine learning revolution grows, demand for machine learning engineers grows with it. Machine learning is a lucrative field to develop your career.In this course, I will teach you how to build a neural network from scratch in 77 lines of Python code. Unlike other courses, we won't be using machine learning libraries, which means you will gain a unique level of insight into how neural networks actually work. This course is designed for beginners. I don't use complex mathematics and I explain the Python code line by line, so the concepts are explained clearly and simply.This is the expanded and improved video version of my blog post "How to build a neural network in 9 lines of Python code" which has been read by over 500,0000 students.Enroll today to start building your neural network.
The Machine Learning Bootcamp for Complete Beginners 2025 is the fastest way to start your journey into Python programming, data science, and machine learning—no prior experience required.We’ll start from the very basics of Python: data types, variables, loops, functions, classes, exceptions, file handling, and test-driven development. You’ll also work with databases and APIs, which are essential for handling real-world data.Once you’re confident with Python, we’ll dive into the heart of machine learning. Step by step, you’ll explore and apply key algorithms:Linear Regression – predicting house and car pricesLogistic Regression – classifying health and customer dataDecision Trees & Random Forests – modeling complex decisionsKMeans Clustering – grouping unlabeled dataPCA (Principal Component Analysis) – reducing dimensions for big dataFinally, you’ll build and deploy a capstone project: a House Price Prediction web app with Flask, bringing everything you’ve learned into a practical, real-world project.This bootcamp focuses on hands-on coding, practical datasets, and real projects so that you’re not just learning theory—you’re building skills you can use right away.Who Is This Course For?This course is designed for:Absolute beginners with no prior coding experience.Students or professionals curious about AI and machine learning.Career changers looking to enter the data science or AI field.Developers who want to strengthen their Python and ML foundations.Anyone who wants to understand how machine learning powers modern apps.What You Will LearnBy the end of this b
Welcome to my comprehensive course on crafting prompts that not only drive engagement but also uphold ethical standards. In today's interconnected world, the ability to formulate queries that are both compelling and responsible is essential.Throughout this course, you'll delve into the intricacies of prompt engineering, exploring diverse topics such as AI ethics, bias awareness, and cultural sensitivity. Whether you're a beginner seeking foundational knowledge or an experienced professional aiming to refine your skills, our curriculum is tailored to meet your needs.Learn how to navigate legal and medical constraints, stimulate creative problem-solving, and craft prompts for educational, societal, and environmental issues. Our expert-level modules delve into advanced techniques such as integrating multiple topics, long-form content generation, and psychological theories in prompt design.Furthermore, our mastery-level sessions provide a deep dive into algorithmic understanding, cross-disciplinary crafting, and ethical dilemmas in prompt formulation. By the end of this course, you'll emerge equipped with the tools and insights needed to drive meaningful conversations, foster innovation, and navigate complex inquiries with precision and integrity.Join me on this journey to master the art of prompt engineering and make a positive impact in your field. Enroll today and unlock the potential to shape dialogue, foster understanding, and drive positive change through thoughtful and ethical prompt design.
Machine learning brings together computer science and statistics to build smart, efficient models. Using powerful techniques offered by machine learning, you’ll tackle data-driven problems. The effective blend of Machine Learning with Python, scikit-learn, and TensorFlow, helps in implementing solutions to real-world problems as well as automating analytical model. This comprehensive 3-in-1 course is your one-stop solution in mastering machine learning algorithms and their implementation. Learn the fundamentals of machine learning and build your own intelligent applications. Explore popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Contents and Overview This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible. This course will help you discover the magical black box that is Machine Learning by teaching a practical approach to modeling using Python, scikit-learn and TensorFlow. The first course, Step-by-Step Machine Learning with Python, covers easy-to-follow examples that get you up and running with machine learning. In this course, you’ll learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. You’ll build your own models from scratch. The second course, Machine Learning with Scikit-learn, covers effective learning algorithms to real-world problems using scikit-learn. You’ll build systems that classify documents, recognize images, detect ads, and more. You’ll learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance; and develop an intuition for how to improve your model’s performance. The third cou
Generative AI: From Fundamentals to Advanced ApplicationsThis comprehensive course is designed to equip learners with a deep understanding of Generative AI, particularly focusing on Large Language Models (LLMs) and their applications. You will delve into the core concepts, practical implementation techniques, and ethical considerations surrounding this transformative technology.What You Will Learn:Foundational Knowledge: Grasp the evolution of AI, understand the core principles of Generative AI, and explore its diverse use cases.LLM Architecture and Training: Gain insights into the architecture of LLMs, their training processes, and the factors influencing their performance.Prompt Engineering: Master the art of crafting effective prompts to maximize LLM capabilities and overcome limitations.Fine-Tuning and Optimization: Learn how to tailor LLMs to specific tasks through fine-tuning and explore techniques like PEFT and RLHF.RAG and Real-World Applications: Discover how to integrate LLMs with external knowledge sources using Retrieval Augmented Generation (RAG) and explore practical applications.Ethical Considerations: Understand the ethical implications of Generative AI and responsible AI practices.By the end of this course, you will be equipped to build and deploy robust Generative AI solutions, addressing real-world challenges while adhering to ethical guidelines. Whether you are a data scientist, developer, or business professional, this course will provide you with the necessary skills to thrive in the era of Generative AI.Course Structure:The course is structured into 12 sections, covering a wide range of topics from foundational concepts to advanced techniques. Each section includes multiple lectures, providing a comp
Welcome to the "Azure Open AI & Prompt Engineering Zero to Hero with Chatgpt" course!In this course, you will learn how to work with Azure OpenAI, specifically the GPT-3.5/4 model and how to use it for prompt engineering, which is the art of crafting effective prompts to generate high-quality text responses. You will start with the basics of Azure OpenAI and progress to more advanced topics, such as prompt engineering, data preparation, and model fine-tuning. By the end of this course, you will have a strong understanding of Azure OpenAI and how to use it for prompt engineering, as well as the skills to build your own powerful AI applications using GPT-3.5.This course is designed for developers and data scientists who are interested in learning how to work with Azure OpenAI and want to become proficient in prompt engineering. Learn about the fundamentals of Azure Open AI and Prompt Engineering.Understand the concept of natural language processing and how it works with AI.Dive deep into the principles of prompt engineering, and how it can be used to generate human-like text.Explore the different tools and platforms offered by Azure for Open AI and Prompt Engineering.Understand the importance of pre-training and fine-tuning in creating robust AI models.Discover how to use GPT-3 models for text completion and generation.Learn how to train and deploy GPT-3 models on Azure.Gain insights into best practices for working with Open AI and Prompt Engineering.Learn about the ethical considerations and potential risks associated with AI and how to mitigate them.By the end of this course, you will have a solid understanding of Azure Open AI and Prompt Engineering and be able to apply this knowledge to create powerful and effective AI models. Whether you are a developer, data scientist, or AI enthusiast, this course will
This beginner's course builds an understanding of the essential math required for data analytics.
Get instant access to a 69-page Machine Learning workbook containing all the reference materialOver 9 hours of clear and concise step-by-step instructions, practical lessons, and engagementIntroduce yourself to our community of students in this course and tell us your goalsEncouragement & celebration of your progress: 25%, 50%, 75%, and then 100% when you get your certificateWhat will you get from doing this course?This course will help you develop Machine Learning skills for solving real-life problems in the new digital world. Machine Learning combines computer science and statistics to analyse raw real-time data, identify trends, and make predictions. You will explore key techniques and tools to build Machine Learning solutions for businesses. You don’t need to have any technical knowledge to learn these skills.What will you learn:What is Machine LearningSupervised Machine LearningUnsupervised Machine LearningSemi-Supervised Machine LearningTypes of Supervised Learning: ClassificationRegressionTypes of Unsupervised Learning: ClusteringAssociationData CollectionData PreparingSelection of a ModelData Training and EvaluationHPT in Machine LearningPrediction in MLDPP in MLNeed of DPPSteps in DPPPython LibrariesMissing, Encoding, and Splitting Data in MLPython, Java, R,and C ++How to install python and anaconda?Interface of Jupyter NotebookMathematics in PythonEuler's Number and VariablesDegree into Radians and Radians into Degrees in PythonPrinting Functions in PythonFeature Scaling for ML<p
Learn The Data Science Course 2025: Complete Data Science Bootcamp
This specialization is a great starting point for beginners who want to learn Python for data science. While not focused solely on regression, it provides the necessary programming foundation to tackle more advanced machine learning courses.
You're looking for a complete Machine Learning and Deep Learning course that can help you launch a flourishing career in the field of Data Science, Machine Learning, Python, R or Deep Learning, right?You've found the right Machine Learning course!After completing this course you will be able to:· Confidently build predictive Machine Learning and Deep Learning models using R, Python to solve business problems and create business strategy· Answer Machine Learning, Deep Learning, R, Python related interview questions· Participate and perform in online Data Analytics and Data Science competitions such as Kaggle competitionsCheck out the table of contents below to see what all Machine Learning and Deep Learning models you are going to learn.How this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.If you are a business manager or an executive, or a student who wants to learn and apply machine learning and deep learning concepts in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning and deep learning. You will also get exposure to data science and data analysis tools like R and Python.Why should you choose this course?This course covers all the steps that one should take while solving a business problem through linear regression. It also focuses Machine Learning and Deep Learning techniques in R and Python.Most courses only focus on teaching how to run the data analysis but we believe that what happens before and after running data analysis is even more important i.e. before running data analysis it is very important that you have the right data and do some pre-processing on it. And after running data analysis, you should be able to judge how good your m
A beginner-level micro-learning course that provides an overview of Large Language Models, including their architecture, frameworks, and applications. It also introduces fundamental NLP concepts.
This course provides an introduction to the concepts of time series analysis, covering topics like stationarity, autocorrelation, and basic models.
In this self-paced course, you will learn how to apply Naive Bayes to many real-world datasets in a wide variety of areas, such as:computer visionnatural language processingfinancial analysishealthcaregenomicsWhy should you take this course? Naive Bayes is one of the fundamental algorithms in machine learning, data science, and artificial intelligence. No practitioner is complete without mastering it.This course is designed to be appropriate for all levels of students, whether you are beginner, intermediate, or advanced. You'll learn both the intuition for how Naive Bayes works and how to apply it effectively while accounting for the unique characteristics of the Naive Bayes algorithm. You'll learn about when and why to use the different versions of Naive Bayes included in Scikit-Learn, including GaussianNB, BernoulliNB, and MultinomialNB.In the advanced section of the course, you will learn about how Naive Bayes really works under the hood. You will also learn how to implement several variants of Naive Bayes from scratch, including Gaussian Naive Bayes, Bernoulli Naive Bayes, and Multinomial Naive Bayes. The advanced section will require knowledge of probability, so be prepared!Thank you for reading and I hope to see you soon!Suggested Prerequisites:Decent Python programming skillComfortable with data science libraries like Numpy and MatplotlibFor the advanced section, probability knowledge is requiredWHAT ORDER SHOULD I TAKE YOUR COURSES IN?Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including my free course)UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeLess than 24 hour
A beginner-friendly course that introduces you to GitHub Copilot, showing how to boost productivity, write smarter code, and integrate AI into your development workflow. You will learn to harness its capabilities for faster, error-free coding.
This course provides an introduction to the engineering of autonomous aerospace systems, with a focus on drones. It covers topics such as modeling, simulation, and control of autonomous vehicles.
Watch the FREE PREVIEW lessons to get a SNEAK PEEK at ChatGPT & Midjourney - start learning how to use these powerful AI Tools right now!ALWAYS UPDATED: This ChatGPT+ Course is Constantly Being Updated. Since launch we've:Added new ChatGPT with the latest releases of Chat GPT 4, Connection to the Internet, Plugins, and moreAdded a complete Midjourney course for creating AI based imagesAdded a complete Google Bard (now Gemini) course for an alternative to ChatGPTAdded lessons on other AI tools like DALL-E, Pictory AI for video creators, and moreAdded lessons on Adobe FireflyWe'll start with the basics, including what ChatGPT, Google Bard, & Midjourney are, why you should use these tools, and how they work. You'll learn how to create your account and get started with ChatGPT, Google Bard, & Midjourney. You will also learn how to use ChatGPT, Google Bard, & Midjourney to improve your business, content, or marketing and strategies.To help you quickly start using ChatGPT, Google Bard, & Midjourney, you'll receive a downloadable list of prompts that you can try out yourself.Key Concepts of this Course:Prompt EngineeringGenerative AIAI Text Content GenerationChatGPT IntensiveGoogle Bard IntensiveMidjourney IntensiveOpen AIDALL-E 2What can ChatGPT, Google Bard, & Midjourney do for you? Here are some examples we'll be covering in this ChatGPT Masters course.AI For Content CreationFind Keywords fo
If you are completely new to the AI world, then this course is for you. Whether you're a student, freelancer, content creator, or working professional, this beginner-friendly course will help you understand and use the tools that are shaping the future of work and communication.At the heart of this course is ChatGPT, a powerful AI chatbot built by OpenAI using advanced Generative AI. ChatGPT can write, explain, brainstorm, summarize, translate, create images, and even help with coding — all through natural, human-like conversations. You’ll learn how to use ChatGPT to speed up your work, generate content, solve problems, and bring ideas to life.But ChatGPT is just one part of a bigger picture. This course also introduces you to the core concept behind it: Generative AI. Generative AI is a type of artificial intelligence that creates new content — not just text, but also images, audio, video, code, and more. To use these tools effectively, you need to learn Prompt Engineering — the essential skill of writing clear, specific instructions (called prompts) to guide AI. Since AI doesn't “understand” like humans do, your input has to be carefully written to get accurate, high-quality results. This course teaches you how to craft effective prompts, provide context, define tone or format, and refine your output step by step.What You'll Learn:ChatGPT fundamentalsChatGPT best practicesChatGPT promptsChatGPT Real-world use casesChatGPT advanced featuresAI FundamentalsGenerative AI FundamentalsPrompt EngineeringPrompt Engineering techniquesWhat is ChatGPT?ChatGPT is a smart AI chatbot developed by OpenAI. It can understand what you type and respond in a natural, human-like way. You can us
Offered by Michigan State University, this specialization provides a thorough introduction to game design and development using Unity. It is a series of courses that includes hands-on projects where you build multiple 2D and 3D games.
This specialization by Stanford University, taught by Andrew Ng, is a highly popular and comprehensive introduction to machine learning. It covers fundamental concepts including Support Vector Machines (SVMs) and kernel methods. The course is designed for beginners and provides a strong theoretical and practical foundation.
This is a 3-course specialization that provides a broad introduction to modern machine learning. It covers supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for AI and machine learning innovation.
Part of the HarvardX Data Science Professional Certificate, this course covers the basics of data visualization and exploratory data analysis using ggplot2 in R.
Welcome to this non-technical training for executives!This exclusive Udemy for Business training is designed to give you a high level overview of the key topics in Data Science and Machine Learning. Designed exclusively for students who want to learn about the basics of data science and machine learning at a high level, without needing to learn how to code or cover complex mathematics.In this course you'll learn the fundamentals to high quality data, allowing you to understand what makes data suitable for analysis and machine learning. Then we'll give you a quick overview of important statistical topics, such as mean, standard deviation, and the normal distribution. Afterwards you will learn the different ways data scientists are able to visualize data to convey their ideas in a clear manner.Once we've learned the basics of data, statistics, and visualization we'll explore the amazing opportunities machine learning has to offer. We'll teach you about the machine learning process, acquiring data, cleaning data, and an overview of the train/test split philosophy that supervised learning adheres to. Then we'll show you some examples of regression and classification algorithms, as well as how to evaluate their results.Once we understand regression and classification, we'll teach you about clustering techniques such as KMeans algorithm and dimensionality reduction methods like Principal Component Analysis.Let's being your first steps into data science and machine learning! Enroll today and we'll see you inside the course!
This course from MIT provides an introduction to computational thinking and data science. You will learn how to use computation to solve problems and explore data, which is a great foundation for machine learning.
Deep learning would be part of every developer's toolbox in near future. It wouldn't just be tool for experts. In this course, we will develop our own deep learning framework in Python from zero to one whereas the mathematical backgrounds of neural networks and deep learning are mentioned concretely. Hands on programming approach would make concepts more understandable. So, you would not need to consume any high level deep learning framework anymore. Even though, python is used in the course, you can easily adapt the theory into any other programming language.
“We are bringing technology to philosophers and poets.”Machine Learning is usually considered to be the forte of professionals belonging to the programming and technology domain. People from arts and social science with no background in programming/technology often find it challenging to learn Machine Learning. However, Machine learning is not for technologists and programmers only. It is for everyone who wants to be a better researcher and decision-maker.Machine Learning is for anyone looking to model how humans and machines make decisions, develop mathematical models of decisions, improve decision-making accuracy based on data, and do science with data.Machine Learning brings you closer to the fascinating world of artificial intelligence. Machine Learning is a cross-disciplinary field encompassing computer science, mathematics, statistics, psychology, and management. It’s currently tough for normal learners to understand so many subjects, making Machine Learning inaccessible to many, especially those from social science backgrounds.We built this course, “Machine Learning for Social Scientists,” to help learners master this topic without getting stuck in its technicalities or fear of coding. This course is built as a scratch to the advanced level course for Machine Learning. All the topics are explained with the basics. The instructor creates a connection with everyday instances and fundamental tools so that learners feel connected to their previous learning. For example, we demo some Excel calculations to ensure learners can see the connection between Excel spreadsheet analysis and Machine Learning using R language.The course covers the following topics:· Fundamentals of Machine Learning· Applications of Machine Learning· Statistical concepts underlying Machine Learning· Supervised Machine Learning Algorithms· Unsupervised Machine Learning Algorithms· How to Use R to Implement Machi
Why study data science?Companies have a problem: they collect and store huge amounts of data on a daily basis. The problem is that they don't have the tools and capabilities to extract knowledge and make decisions from that data. But that is changing. For some years now, the demand for data scientists has grown exponentially. So much so, that the number of people with these skills is not enough to fill all the job openings. A basic search on Glassdoor or Indeed will reveal to you why data scientist salaries have grown so much in recent years.Why this course?Almost every course out there is either too theoretical or too practical. University courses don't usually develop the skills needed to tackle data science problems from scratch, nor do they teach you how to use the necessary software fluently. On the other hand, many online courses and bootcamps teach you how to use these techniques without getting a deep understanding of them, going through the theory superficially.Our course combines the best of each method. On the one hand, we will look at where these methods come from and why they are used, understanding why they work the way they do. On the other, we will program these methods from scratch, using the most popular data science and machine learning libraries in Python. Only when you have understood exactly how each algorithm works, we will learn how to use them with advanced Python libraries.Course contentIntroduction to machine learning and data science.Simple linear regression. We will learn how to study the relationship between different phenomena.Multiple linear regression. We will create models with more than one variable to study the behavior of a variable of interest.Lasso regression. Advanced version of multiple linear regression with the ability to filter the most useful variables.Ridge regression. A
Werde zum gefragten Data-Science-Spezialisten mit R!Data-Science-Experten sind nicht nur gefragt wie nie, sie bekommen auch ein überdurchschnittliches Gehalt (laut Indeed Jobbörse). Diesen Kurs habe ich entwickelt, um dir den bestmöglichen Einstieg zu bieten.R ist eine unglaublich mächtige und effiziente Sprache, sowohl ob für Data Science als auch Machine Learning. Leider ist der Einstieg allerdings oft sehr trocken - nicht aber in diesem Kurs, alle Themen lernst du Schritt für Schritt und am Beispiel.=> "Wie auch bei Jannis' anderen Kursen ist alles top! Gute step by step Introduction." (★★★★★, Markus Dunkel)Besonders viele Übungen + Beispiele:In diesem Kurs werden alle Themen anschaulich erklärt - du analysierst Geburtsstatistiken & echte Gehälter aus San Francisco, erstellst ein Modell für Diabetes, extrahierst Raketenstarts aus einer Webseite (Web-Crawling) oder visualisierst in einer Grafik die Ausbreitung von Ebola bzw. dem Coronavirus. Schritt für Schritt lernst du also alles was du zum Thema R wissen musst - und zwar nicht nur die Sprache selbst, sondern auch alle wichtigen Tools drumherum, und wie R angewandt wird. Dadurch kannst du das Wissen aus dem Kurs sofort anwenden. Mit über 200+ HD-Videos und mehr als 23 Stunden Videomaterial ist dies der umfangreichste Data-Science Kurs mit R auf Udemy.Was lernst du alles?R Grundlagen:RStudio (unsere Entwicklungsumgebung)FunktionenVariablen,...Data Science:Lese Daten einErstelle anschauliche VisualisierungenÜberzeuge deine Kollegen durch überzeugende PDF-ReportsDiverse Beispiele!Machine Learning mit caret:Regre
This is an ambitious course. The goal here is simple: Only teach what you need to know for day 1 of your first data science job. No fluff, nothing out of context, no topics that are not relevant to real world applications. We will cover EVERY core topic and tool required for those new to data science: Python, R, SQL, Useful Math/Stats/Algorithms, Tableau, and Excel in depth. The course will cover skills that align with three different job types:- Data Analyst- General Data Scientist- Machine Learning EngineerYou can expect to learn from first principles the foundational topics and tools used in practice today. We will avoid topics that are not useful or are simply too advanced when starting out. Your journey will be guided by the Data Science Road Map, a collection of the best resources gathered through years of experience by the instructor.In addition, we will survey every important technology required on the job including GitHub, Kaggle, the basics of cloud, web development and docker. With over 200 videos, readings, and assignments, you can be sure you will be well prepared to join the data community.If you are just getting started or want to fill in some of your knowledge gaps this course is for you!
TensorFlow has been gaining immense popularity over the past few months, due to its power and simplicity to use. So, if you’re a Python developer who is interested in learning how to create applications and perform image processing using TensorFlow, then you should surely go for this Learning Path. Packt’s Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. The highlights of this Learning Path are: Learn how to create image processing applications using free tools and librariesPerform advanced image processing with TensorFlowAPIsUnderstand and optimize various features of TensorFlow by building deep learning state-of-the-art models Let's take a quick look at your learning journey. This Learning Path starts off with an introduction to image processing. You will then walk through graph tensor which is used for image classification. Starting with the basic 2D images, you will gradually be taken through more complex images, colors, shapes, and so on. You will also learn to make use of Python API to classify and train your model to identify objects in an image. Next, you will learn about convolutional neural networks (CNNs), its architecture, and why they perform well in the image take. You will then dive into the different layers available in TensorFlow. You will also learn to construct the neural network feature extractor to embed images into a dense and rich vector space. Moving ahead, you will learn to construct efficient CNN architectures with CNN Squeeze layers and delayed downsampling. You will learn about residual learning with skip connections and deep residual blocks, and see how to implement a deep residual neural network for image recognition. Next, you will find out about Google’s Incep
Linear Algebra is one of the essential foundations for anyone who wants to work in Data Science and Artificial Intelligence. Whether manipulating large datasets, building predictive models, or implementing Machine Learning algorithms, a solid understanding of this mathematical field is indispensable. This course is designed to provide an intuitive and practical approach to the most important concepts, combining theory and Python implementations to ensure you learn by applying. The course is divided into six sections, each covering a fundamental aspect of Linear Algebra. We begin with an introduction to core concepts, explaining the importance of this discipline and how it connects to Data Science and Machine Learning. Here, we cover elements like scalars, vectors, matrices, and tensors, along with setting up the necessary Python libraries. We also explore data representation and how linear systems are used to solve mathematical problems. In the second section, we dive deeper into vectors—their properties and applications. Vectors are fundamental components in data manipulation, feature scaling, and even defining the multidimensional spaces used in predictive models. You’ll learn about norms, unit vectors, orthogonal and orthonormal vectors, and visualize these structures intuitively through graphs. Next, we explore matrices, which are widely used to represent data and process large volumes of information. We’ll cover key matrix properties, norms, transposition, inversion, and essential decompositions for diverse applications. These concepts are critical for neural networks, linear regressions, and dimensionality reduction techniques. The fourth section focuses on operations involving vectors and matrices. We’ll study matrix multiplication, dot and cross products, reduction operations, and the cosine rule—essential tools for calculating data similarity and efficiently manipulating mathematical structures. Then, we tackle linear tr
Disclaimer:The second of this course demonstrates techniques using Jupyter Notebooks from Anaconda. You are welcome to follow along (however), it is not required to do these exercises to complete this course. If you are a Udemy Business user, please check with your employer before downloading software.Welcome!: Thank you all for the huge response to this emerging course! We are delighted to have over 20,000 students in over 160 different countries. I'm genuinely touched by the overwhelmingly positive and thoughtful reviews. It's such a privilege to share and introduce this important topic with everyday people in a clear and understandable way. I'm also excited to announce that I have created real closed captions for all course material, so weather you need them due to a hearing impairment, or find it easier to follow long (great for ESL students!)... I've got you covered. Most importantly: To make this course "real", we've expanded. In November of 2018, the course went from 41 lectures and 8 sections, to 62 lectures and 15 sections! We hope you enjoy the new content! Unlock the secrets of understanding Machine Learning for Data Science!In this introductory course, the “Backyard Data Scientist” will guide you through wilderness of Machine Learning for Data Science. Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the “techno sphere around us”, why it’s important now, and how it will dramatically change our world today and for days to come. Our exotic journey will include the core concepts of:The train wreck definition of computer science and one that will actually instead make sense. An explanation of data that will have you seeing data everywhere t
A Álgebra Linear é um dos fundamentos essenciais para quem deseja atuar com Ciência de Dados e Inteligência Artificial. Seja na manipulação de grandes conjuntos de dados, na construção de modelos preditivos ou na implementação de algoritmos de Machine Learning, a compreensão dessa área matemática é indispensável. Este curso foi estruturado para oferecer uma abordagem intuitiva e prática dos conceitos mais importantes, combinando teoria e implementações em Python para garantir que você aprenda aplicando.O curso é dividido em seis seções, cada uma abordando um aspecto fundamental da Álgebra Linear. Começamos com uma introdução aos conceitos básicos, onde explicamos a importância dessa disciplina e como ela se conecta com Data Science e Machine Learning. Aqui, são apresentados elementos como escalares, vetores, matrizes e tensores, além da instalação das bibliotecas necessárias para a programação em Python. Também exploramos a representação de dados e como os sistemas lineares são utilizados para resolver problemas matemáticos.Na segunda seção, aprofundamos o estudo dos vetores, suas propriedades e aplicações. Vetores são componentes fundamentais na manipulação de dados, na normalização de variáveis e até mesmo na definição de espaços multidimensionais usados em modelos preditivos. Você aprenderá sobre normas, vetores unitários, vetores ortogonais e ortonormais, além de visualizar essas estruturas de maneira intuitiva através de gráficos.Em seguida, exploramos as matrizes, que são amplamente utilizadas na representação de dados e no processamento de grandes volumes de informações. Conheceremos as principais propriedades das matrizes, suas normas, transposição, inversão e decomposições fundamentais para diversas aplicações. Esses conceitos são indispensáveis para o funcionamento de redes neurais, regressões lineares e técnicas de redução de dimensionalidade.A quarta seção é dedicada às operações envolvendo vetores e matrizes</st
Computer Vision With Deep Learningرؤية الكمبيوتر باستخدام التعلم العميقDescriptionThis is a complete course that will prepare you to work in Computer Vision Using Deep Learning. We will cover the fundamentals of Deep Learning/ computer Vision and its applications, this course is designed to reduce the time for the learner to Learn Computer Vision using Deep learning.هذه دورة كاملة ستعدك للعمل في رؤية الكمبيوتر باستخدام التعلم العميق. سنغطي أساسيات التعلم العميق/رؤية الكمبيوتر وتطبيقاتها، وقد تم تصميم هذه الدورة لتقليل الوقت الذي يستغرقه المتعلم لتعلم رؤية الكمبيوتر باستخدام التعلم العميق.What Skills will you Learn:In this course, you will learn the following skills:Understand the Math behind Deep Learning Algorithms.Understand How computer vision Algorithms works.Write and build computer vision Algorithms using Deep learning technologies.Use opensource libraries.We will cover:Fundamentals of Computer Vision.Image Preprocessing.Deep Neural Network (DNN) - Pytorch . Convolutional Neural Network (CNN)- TensorFlow.Semantic Segmentation.Object Detection.Instance Segmentation.Pose Estimation.Generative AI.Face Recognition.If you do not have prior experience in Machine Learning OR Computer vision, that's NO PROBLEM!. This course is complete and concise, covering the fundamental Theory and needed coding knowledge. Let's work together to learn Computer Vision Using Deep Learning.إذا لم تكن لديك خبرة سابقة في التعلم الآلي أو رؤية الك
Dans ce cours accéléré, nous allons aborder les opportunités qu'offrent les modèles génératifs et ensuite, nous nous intéresserons plus particulièrement aux Generative Adversarial Networks (GANs). Je vais vous expliquer le fonctionnement des GANs de manière intuitive et ensuite, nous nous plongerons dans l'article qui les a introduit en 2014 (Ian J. Goodfellow et al.). Je vous expliquerai donc de manière mathématique le fonctionnement des GANs, ce qui vous permettra d'avoir les bases nécessaires pour implémenter votre premier GAN en partant de zéro.Nous implémenterons en approximativement 100 lignes de code un générateur, un discriminateur et le pseudo-code décrit dans l'article afin d'entraîner ces derniers. Nous utiliserons le langage de programmation Python et le framework PyTorch. Après entraînement, le générateur nous permettra de générer des images synthétiques.J'ai la conviction qu'un concept s'apprend par la pratique et ce cours accéléré a pour objectif de vous donner les bases nécessaires afin de continuer votre apprentissage du Machine Learning, de PyTorch et des modèles génératifs (GANS, Variational Autoencoders, Normalizing Flows, ...).À l'issue de ce cours, le participant aura la possibilité d'utiliser Python (et plus particulièrement le framework PyTorch) afin d'implémenter des articles scientifiques et des solutions d'intelligence artificielle. Ce cours a également pour objectif d'être un tremplin dans votre apprentissage des modèles génératifs.Au-delà des GANs, ce cours est également une introduction générale au framework PyTorch et un cours de Machine learning de niveau intermédiaire .Concepts abordés:Le framework PyTorch afin d'implémenter et d'optimiser des réseaux de neurones.Le framework Keras afin de charger un ensemble de données.Google colab.L'utilisation des modèles génératifs dans le monde de la recherche et industri
Introduction to generative AI including LLMs, image generation, and practical applications of Gen AI technology.
A beginner-friendly Python course with interactive coding exercises. Build a solid foundation in Python programming.
Master prompt engineering techniques for ChatGPT and other LLMs. Learn advanced prompting strategies.
Master the essentials of MCP, build servers and clients, and deploy scalable, context-aware AI agents through hands-on development.
Explore AI fundamentals, history, models, and ethics in our generative AI course and gain the skills to innovate and lead in an AI-driven future.
This course covers the GenAI revolution and its role in securing a career with AWS Certified AI Practitioner AIF-C01, focusing on AI services, machine learning, and cloud AI.
Learn how large language models work, from inference and training to prompting, embeddings, and RAG. Build practical skills to apply LLMs effectively in real-world language applications.
Discover how to leverage LangChain for the development of LLM-powered applications. Learn about prompt templates, chains, memory types, and tools to build AI applications.
Learn n8n for advanced automation. Build intelligent workflows using dynamic logic, AI agents, LLMs, and RAG systems. Then, integrate with APIs and deploy to production.
Learn GitHub Copilot foundations through hands-on lessons: explore AI-assisted coding, Copilot chat, prompt engineering, code reviews, testing, and debugging to incorporate AI into your workflows.
Gain proficiency in Claude Code for AI-powered development: from setup to automation, sub-agents, and GitHub integration.
This course covers how Cursor uses integrated AI tools to help modern developers speed up coding, debugging, and project management.
Explore the free generative AI course and familiarize yourself with the concepts of LLMs, fine-tuning, RAG, vector DBs, diffusion models, chatbots, agents, and prompting techniques.
Explore this beginner RAG course to learn the basics of retrieval-augmented generation. For hands-on practice, build RAG pipelines using LangChain and create user-friendly applications with Streamlit.
This course provides a review of the basics of linear models and matrix algebra, which are foundational concepts for understanding regression methods.
This course teaches how to use LlamaIndex to connect with large language models, build RAG systems, extract data, and create agentic and AI applications.
Build intelligent, multimodal, and agentic apps using OpenAI APIs. Gain hands-on experience with text, audio, image, and agent development through secure, production-ready workflows.
In this free course, you will learn the fundamentals of chatbots with Python. You will build secure, AI-powered chatbots step-by-step for learning and experimentation.
Learn setup, code generation, debugging, Git, and testing with agentic tools like Cascade to boost productivity inside your Windsurf IDE.
Lead the GenAI revolution by exploring OpenAI's API and mastering ChatGPT. Gain hands-on experience, implement innovative applications, and future-proof your skills for the AI-driven future.
In this diffusion models course, you will explore their workings and architecture and learn to create images from noise using neural networks and pretrained models with practical implementations.
This prompt engineering for marketing course shows solopreneurs and small businesses how to leverage prompt engineering for market research, SEO, marketing campaigns, and customer service.
In this spaCy NLP course, you will learn about core tasks like tokenization, NER, and POS tagging and advanced topics such as custom model training and complex NLP pipelines.
Gain insights into Llama 3 by learning prompting techniques, mastering control parameters, exploring real-world applications, and addressing ethical challenges to enhance efficiency in various creative tasks.
Explore this Gemini course to master Google Gemini's AI features, including text-to-text and image-to-text. Build apps, learn prompting techniques, and enhance workflows with tools like Vertex AI.
Lead the GenAI revolution, future-proof your skills by exploring generative models, transformer networks, and state-of-the-art applications in text, image, and video generation.
In this course, you'll learn how to build a professional portfolio using prompt engineering, using generative AI to craft impactful cover letters, resumes, emails, and an optimized LinkedIn profile.
Explore this AI chatbots course to build Python-based multimodal chatbots with Gradio, Rasa, Gemini, and Whisper v3. Learn LLM-powered techniques, RAG integration, and deploy on Hugging Face.
Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science. The course covers the basics of linear regression and its application in real-world scenarios.
Learn to build applications with Gemini Code Assist, an AI collaborator for developers. Use its IDE and CLI to scaffold apps, debug, refactor, generate tests, and integrate with version control.
Learn the OpenAI API with Python and NLP by mastering API endpoints, text generation, moderation, embeddings, and Flask integration for real-world tasks.
Learn to integrate the OpenAI API with JavaScript and React for NLP tasks like text generation, classification, moderation, and embeddings in dynamic apps.
This course from the University of Toronto provides a hands-on introduction to quantum-enhanced machine learning. It covers the intersection of quantum computing and machine learning, focusing on algorithms that are challenging for classical computers. The course emphasizes implementing protocols using open-source Python frameworks and features guest lectures from prominent researchers in the field.
An in-depth introduction to time series analysis, covering structured models, predictions, and reinforcement learning with hands-on projects. This course is part of the MITx MicroMasters program in Statistics and Data Science.
This program from UC Berkeley provides a comprehensive introduction to data science, including data wrangling and cleaning, using Python.
This course focuses on recognizing and solving convex optimization problems that arise in applications. Topics include convex sets, functions, and optimization problems; basics of convex analysis; and applications in signal processing, machine learning, and finance.
This course provides a comprehensive introduction to data cleaning with Python. You'll learn to diagnose your data's dirtiness, and develop the skills to clean it. You'll deal with common data problems like missing values, inconsistent data types, and duplicates.
Hello there,Machine learning python, python, machine learning, Django, ethical hacking, python bootcamp, data analysis, machine learning python, python for beginners, data science, machine learning, django:Welcome to the “Python: Machine Learning, Deep Learning, Pandas, Matplotlib” course. Python, Machine Learning, Deep Learning, Pandas, Seaborn, Matplotlib, Geoplotlib, NumPy, Data Analysis, TensorflowPython instructors on Udemy specialize in everything from software development to data analysis and are known for their effective, friendly instruction for students of all levels.Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. Whether you’re a marketer, video game designer, or programmer, this course is here to help you apply machine learning to your work.In this course, we will learn what is Deep Learning and how does it work.This course has suitable for everybody who is interested in Machine Learning and Deep Learning concepts in Data Science.First of all, in this course, we will learn some fundamental stuff of Python and the Numpy library. These are our first steps in our Deep Learning journey. After then we take a little trip to Machine Learning Python history. Then we will arrive at our next stop. Machine Learning in Python Programming. Here we learn the machine learning concepts, machine learning a-z workflow, models and algorithms, and what is neural network concept. After then we arrive at our next stop. Artificial Neural network. And now our journey becomes an adventure. In this adventure we'll enter the Keras world then we exit the Tensorflow world. Then we'll try to under
This course provides a comprehensive introduction to Data Version Control (DVC) for managing and versioning machine learning data. Students will learn about the machine learning product lifecycle and the differences between data and code versioning.
This course provides a gentle introduction to deep learning using PyTorch. It covers the fundamentals of neural networks and how to build and train them for tasks like image classification.
This course provides a thorough introduction to the caret package in R for building and evaluating supervised learning models.
This course provides a comprehensive introduction to the scikit-learn library, the most popular Python library for machine learning. You'll learn how to use scikit-learn for a variety of machine learning tasks, including regression.
Learn the basics of time series analysis in Python, including concepts like autocorrelation, stationarity, and how to use the statsmodels library for modeling and forecasting.
This course delves into more advanced techniques for time series forecasting, going beyond the basics to cover more complex models and scenarios.
This beginner-level course teaches how to develop curation files to document information about datasets and related business processes. It is a foundational course for anyone looking to get into data curation.
This course provides a practical introduction to machine learning using Python. It covers the entire machine learning workflow, from data preparation and feature engineering to model building and evaluation.
This is the first in a series of courses on statistics foundations from LinkedIn Learning. This course covers the basics of descriptive statistics, including measures of central tendency and variability. The course is designed for beginners.
A beginner-level course teaching how to moderate text and image content for harmful or inappropriate material using Azure AI Content Safety. The course covers creating a Content Safety instance, performing text and image content moderation, and using prompt shields to detect indirect attacks.
A beginner-level module that showcases how AI is transforming accessibility for individuals with disabilities, impacting job roles, and aiding humanitarian efforts through real-world examples.
This collection of resources on Microsoft Learn teaches the basics of AI as it applies to startups, including using Microsoft Copilot to generate ideas, prototype a product, and create a business model.
This Microsoft Learn path provides a comprehensive introduction to Azure AI Document Intelligence (formerly Form Recognizer). You'll learn how to use pre-built models for common document types, train custom models for your specific needs, and integrate the service into your applications to automate document processing workflows.
This free course teaches you the fundamentals of linear regression and its implementation in Python. It is a beginner-friendly course that covers the theory and practical aspects of this important machine learning algorithm.
This free course for beginners covers the basics of sentiment analysis using Python, including text pre-processing, vectorization, and modeling. It has a high number of learners.
Welcome to "ChatGPT for Pros: Generative AI and Prompt Engineering"—a comprehensive, beginner-friendly course designed to help you unlock the full potential of ChatGPT and Generative AI. Whether you're an aspiring entrepreneur, freelancer, content creator, or professional, this course will teach you how to effectively use AI tools to enhance your productivity and generate income across multiple industries.In this course, you'll begin with the basics of ChatGPT, learning how to set up your account and navigate the interface. You'll explore the foundational concepts of Generative AI, understanding how it works and how it can be applied to streamline your workflows. As you progress, you’ll dive deep into Prompt Engineering, discovering how to craft effective and specific prompts to maximize the quality of ChatGPT’s output.You’ll learn how to harness the power of ChatGPT for diverse applications such as content creation, including writing blogs, eBooks, and social media posts, as well as crafting compelling copy for marketing campaigns. The course also covers using Generative AI for freelancing, where you'll discover how to optimize your profile on platforms like LinkedIn, design a portfolio website, and find clients using AI-driven tools. Additionally, you will learn how to create passive income through blogging and digital product creation, allowing you to generate revenue with minimal ongoing effort.We also explore how to integrate Generative AI into business automation, showing you how to automate tasks like social media scheduling, email marketing, and customer support, freeing up more of your time to focus on growth. By the end of this course, you’ll be equipped with the skills to use ChatGPT to drive business success, wh
The original Stanford ML course taught by Andrew Ng
A hands-on introduction to conversational interaction, covering the components, implementation techniques, and evaluation of conversational systems such as chatbots, spoken dialogue systems, and social robots.
Learn Duke University Introduction to Machine Learning
Your journey to AI and ChatGPT success begins here! This course will take you from a beginner to someone who knows how AI and ChatGPT and Generative AI and Prompt Engineering work, knows how to use the power of AI tools to master productivity, and feel really confident about writing effective prompts. This course is designed for those who want to learn the complete ChatGPT, Generative AI and Prompt Engineering. It covers everything to learn AI. I have been using AI tools for my work. I have gained the knowledge in AI. I decided to create this course and share my knowledge and best-kept secrets - to debunk the AI myths, set the record straight, share my experiences with ChatGPT, and all the lessons I’ve learned along the way, and of course, show you how easy it can become to boost your productivity and expand your creativity with the help of AI.You will learn how to use ChatGPT to generate content more productively, beat creative blocks, and optimize your creative process. We will also look at how to quickly research new topics, how to summarize long content formats and repurpose them, and whether it is possible to scale your business thanks to AI. This course will teach you the basics, the advanced stuff, and everything in between. Join me, and we will uncover the truth about AI, ChatGPT, and all the AI-powered tools. I hope you got excited already because, well, there’s a lot to uncover! Let’s go!
Learn Learn Python 3
This course from the University of Leeds provides an introduction to statistical thinking and data analysis using the R programming language. You will learn about data visualization, summary statistics, and hypothesis testing.
A free online course designed for both beginners and professionals, covering the fundamentals of Support Vector Machines with solved problems and examples.
Image classification models find themselves in different places today, like farms, hospitals, industries, schools, and highways,... With the creation of much more efficient deep learning models from the early 2010s, we have seen a great improvement in the state of the art in the domain of image classification.In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step approach. We shall start by understanding how image classification algorithms work, and deploying them to the cloud while observing best practices. We are going to be using Tensorflow 2 (the world's most popular library for deep learning, built by Google) and HuggingfaceYou will learn:The Basics of Tensorflow (Tensors, Model building, training, and evaluation)Deep Learning algorithms like Convolutional neural networks and Vision TransformersEvaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)Mitigating overfitting with Data augmentationAdvanced Tensorflow concepts like Custom Losses and Metrics, Eager and Graph Modes and Custom Training Loops, TensorboardMachine Learning Operations (MLOps) with Weights and Biases (Experiment Tracking, Hyperparameter Tuning, Dataset Versioning, Model Versioning)Binary Classification with Malaria detection Multi-class Classification with Human Emotions DetectionTransfer learning with modern Convnets (Vggnet, Resnet, Mobilenet, Efficientnet)Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)If you are
Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine Learning is the most in-demand and Highest Paying job of 2017 and the same trend will follow for the coming years. With an average salary of $120,000 (Glassdoor and Indeed), Machine Learning will help you to get one of the top-paying jobs. This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science! At the end of the course you will be able to Master Machine Learning using PythonDemystifying Artificial Intelligence, Machine Learning, Data ScienceExplore & Define a ML use caseML Business Solution BlueprintExplore Spyder, Pandas and NumPyImplement Data EngineeringExploratory Data Analysis Introduction to Statistics and Probability DistributionsLearn Machine Learning MethodologyUnderstand Supervised Learning Supervised LearningImplement Simple & Multiple Linear RegressionDecision TreesRegression & Classification Model EvaluationCross Validation, Hyperparameter Ensemble ModelingRandom Forest & XGBoost Learning Machine Learning is a definite way to advance your career and will open doors to new Job opportunities. 100% MONEY-BACK GUARANTEE This course comes with a 30-day money back guarantee. If you're not happy, ask for a refund, all your money back, no questions asked. Feel forward to have a look at course description and demo videos and we look forward to see you inside.
Sentiment analysis and machine translation models are used by millions of people every single day. These deep learning models (most notably transformers) power different industries today. With the creation of much more efficient deep learning models, from the early 2010s, we have seen a great improvement in the state of the art in the domains of sentiment analysis and machine translation.In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step approach. We shall start by understanding how to process text in the context of natural language processing, then we would dive into building our own models and deploying them to the cloud while observing best practices. We are going to be using Tensorflow 2 (the world's most popular library for deep learning, built by Google) and HuggingfaceYou will learn:The Basics of Tensorflow (Tensors, Model building, training, and evaluation).Deep Learning algorithms like Recurrent Neural Networks, Attention Models, Transformers, and Convolutional neural networks.Sentiment analysis with RNNs, Transformers, and Huggingface Transformers (Deberta)Transfer learning with Word2vec and modern Transformers (GPT, Bert, ULmfit, Deberta, T5...)Machine translation with RNNs, attention, transformers, and Huggingface Transformers (T5)Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)If you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals!This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much em
The ChatGPT Masterclass Zero to Hero is a comprehensive course designed to equip you with the necessary skills and knowledge to master the art of communication through text. This course is ideal for individuals who want to improve their software development skills, and writing skills, enhance their online presence, or simply communicate more effectively in today's digital world.Throughout this course, you will be guided by ChatGPT and also applying Generative AI, a large language model trained by OpenAI, based on the GPT-3.5 architecture. ChatGPT will serve as your personal tutor, providing you with customized feedback and guidance every step of the way.Participate in Practice test to test your learning skillsupdate: Learn the top AI tools every professional needs to boost productivityThe course is divided into several modules, each covering a specific topic related to the development and text communication. These modules include:Introduction to ChatGPT - In this module, you will learn the basics of ChatGPT text communication and its importance in today's world. You will also learn about the different types of text communication and their respective applications.Developer Techniques - In this module, you will learn about the different techniques that can be used to improve your software development skills. You will learn about programming, website creation, code debugging, and other aspects of writing that can make your code development more effective.Understanding your Business - In this module, you will learn about the importance of understanding your SEO when communicating through a website. You will learn how to tailor your message to different audiences and how to use language to effectively convey your message to scale your business.ChatGPT for Students - I
The "Machine Learning and Data Science Diploma using Python" is a unique program that enriches Arabic content in the field of artificial intelligence. It's a comprehensive training course centered on interaction, practical application, thorough explanation, and detailed algorithms starting from scratch. The course ensures a robust understanding of algorithms leading to practical implementation, aiding in building strong models applicable to real-life scenarios. It caters to beginners and anyone intrigued by data science, its analysis, and the study of machine learning and artificial intelligence, including Data Analysts, Data Scientists, Machine Learning Engineers, and AI EngineersThis diploma not only equips you with the proficiency to learn machine learning and data science through coding but also ensures a solid grasp of the mathematics behind the algorithms. This understanding is essential for fine-tuning algorithmic parameters effectively.Topics covered in this diploma include:Definition of DiplomaLinear Algebra for Machine LearningData Exploration and PreparationProbability and Statistics for Data ScienceNumPy LibraryPandas LibraryVisualization Libraries (matplotlib, seaborn)Introduction to Machine Learning ConceptsNumerical OptimizationRegression with Different MethodsEnd-to-End Machine Learning ProjectsRegularizationKaggle PlatformClassification (Binary, Multiclass, different metrics)K-Nearest NeighborsNaive BayesLogistic RegressionSupport Vector MachinesDecision TreesEnsemble Learning (Voting, Bagging, Boosting)Hyperparameters TuningPractical ProjectsWhat C
Unlock the power of image- and video-based AI in 2025 with 20+ real-time projects that guide you from foundational theory to fully functional applications. Designed for engineering and science students, STEM graduates, and professionals switching into AI, this hands-on course equips you with end-to-end computer vision skills to build a standout portfolio.Key Highlights:Environment Setup & Basics: Install Python, configure VS Code, and master OpenCV operations—image I/O, color spaces, resizing, thresholding, filters, morphology, bitwise ops, and histogram equalization.Core & Advanced Techniques: Implement edge detection (Sobel, Canny), contour/corner/keypoint detection, texture analysis, optical flow, object tracking, segmentation, and OCR with Tesseract.Deep Learning Integration: Train and deploy TensorFlow/Keras models (EfficientNet-B0) alongside YOLOv7-tiny and YOLOv8 for robust detection tasks.GUI Development: Build interactive Tkinter interfaces to visualize live video feeds, detection results, and system dashboards.20+ Hands-On Projects Include:Smart Face Attendance with face enrollment, embedding extraction, model training, and GUI integration.Driver Drowsiness Detection using EAR/MAR algorithms and real-time alert dashboards.YOLO Object & Weapon Detection pipelines for live inference and visualization.People Counting & Entry/Exit Tracking with configurable line-coordinate logic.License-Plate & Traffic Sign Recognition leveraging Roboflow annotations and custom model training.Intrusion & PPE Detection for workplace safety monitoring.Accident & Fall Detection
If you want to learn the process to detect whether a person is wearing a face mask using AI and Machine Learning algorithms then this course is for you.In this course I will cover, how to build a Face Mask Detection model to detect and predict whether a person is wearing a face mask or not in both static images and live video streams with very high accuracy using Deep Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model using CNN and OpenCV.This course will walk you through the initial data exploration and understanding, Data Augumentation, Data Generators, customizing pretrained Models like MobileNetV2, model building and evaluation. Then using the trained model to detect the presence of face mask in images and video streams.I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.Task 1 : Project Overview.Task 2 : Introduction to Google Colab.Task 3 : Understanding the project folder structure.Task 4 : Understanding the dataset and the folder structure.Task 5 : Loading the data from Google Drive.Task 6 : Importing the Libraries.Task 7 : About Config and Resize File.Task 8 : Some common Methods and UtilitiesTask 9 : About Data Augmentation.Task 10 : Implementing Data Augmentation techniques.Task 11 : About Data Generators.Task 12 : Implementing Data Generators.Task 13 : About Convolutional Neural Network (CNN).Task 14 : About OpenCV.Task 15 : Understanding pre-trained models.Task 1
A comprehensive and free course on linear algebra, covering everything from the basics to more advanced topics.
Learn Intro to Programming: JavaScript
Embark on a comprehensive journey into the world of AI agents with LangGraph. This course is designed to guide you from fundamental concepts to advanced techniques, equipping you with the skills to build sophisticated AI systems. Starting with the core principles, you'll learn about graphs, nodes, edges, and states, and see how they form the foundation of LangGraph workflows. The course begins with constructing a basic agent, allowing you to grasp the essentials through hands-on practice.Next, you'll dive deeper by building a News Writer Agent, enhancing your understanding by integrating state and tools into your agents. The focus will be on practical applications, ensuring you can visualize and test your agents effectively. Finally, the course introduces advanced techniques, including reflection, human-in-the-loop processes, checkpointers, and threads. You'll also learn to incorporate custom tools, adding versatility and functionality to your agents. Whether you're a beginner or looking to advance your skills, this course provides a structured, step-by-step approach to mastering AI agent development with LangGraph.The goal of this course is to equip you with the understanding and skills you need to build your own agents. There are plenty of off-the-shelf agents available via LangGraph and other resources. However, in our experience, when building agents for production you will need to be able to customize. At the end of this course, it is our goal to make sure that you are capable of building your own custom workflows in LangGraph.Note: Prior python programming experience and some experience with LangChain are required for this course.
Immerse yourself in the cutting-edge world of deep learning with TensorFlow through this comprehensive masterclass. Starting with an insightful overview and the scenario of perceptron, progress to creating neural networks, performing multiclass classification, and gaining a deep understanding of convolutional neural networks (CNN). Explore image processing, convolution intuition, and classifying photos of dogs and cats using TensorFlow. Understand the layers of deep learning neural networks and harness the power of transfer learning for advanced concepts. Engage in real-world projects like Face Mask Detection and Linear Model Implementation. Elevate your skills to master TensorFlow, enabling you to build and deploy powerful deep learning models.This masterclass is designed for individuals passionate about deep learning, whether beginners or experienced practitioners. Uncover the secrets of TensorFlow and take your understanding of deep learning to new heights!Section 1: Machine Learning ZERO to HERO - Hands-on with TensorFlowThis foundational section serves as a comprehensive introduction to machine learning using TensorFlow. It begins with essential concepts, including understanding the fundamentals of machine learning and how machines learn. The section then progresses to practical aspects, guiding learners through setting up their workstations, exploring different programming languages, and understanding the functions of Jupyter notebooks. The focus expands to include third-party libraries, with an emphasis on NumPy and Pandas for efficient data manipulation and analysis. The section concludes by introducing data visualization using Matplotlib and Seaborn, providing a solid groundwork for the subsequent sections.Section 2: Project On TensorFlow - Face Mask Detection ApplicationIn this hands-on project section, learners apply their knowledge to a real-world application by building a Face Mask Detection application using TensorFlo
Data is at the heart of our digital economy and data science has been ranked as the hottest profession of the 21st century. Whether you are new to the job market or already in the workforce and looking to upskill yourself, this five course Data Science with Python Professional Certificate program is aimed at preparing you for a career in data science and machine learning. No prior computer programming experience required!You will start by learning Python, the most popular language for data science. You will then develop skills for data analysis and data visualization and also get a practical introduction in machine learning. Finally, you will apply and demonstrate your knowledge of data science and machine learning with a capstone project involving a real life business problem.This program is taught by experts and focused on hands-on learning and job readiness. As such you will work with real datasets and will be given no-charge access to tools like Jupyter notebooks in the IBM Cloud. You will utilize popular Python toolkits and libraries such as pandas, numpy, matplotlib, seaborn, folium, scipy, scikitlearn, and more.Start developing data and analytical skills today and launch your career in data science!This course is highly practical but it won't neglect the theory. we'll start with python basics, and then understand the complete concept of environment , variables , loops , conditions and more advance concept of python programming and machine learning and we install the needed software (on Windows, Linux and Mac OS X), then we'll dive and start python programming straight away. From here onward you'll learn everything by example, by analyzing and practicing different concepts such as operator, operand, conditional statements, looping ,data management .etc, so we'll never have any boring dry theoretical lectures.The course is divided into a number of sections, each section covers a complete python programming field and complete machine lear
Welcome to the Data Science Projects - Data Analysis & Machine Learning course. Data science projects course series is made from the projects that i built for my website and courses. This is not a beginner level course. This course is built for the students who learned python for data science and wants to apply what they learned but don't know where to start or for the ones who wants to practice and test their knowledge. In this course we will be building 4 data science projects which are going to be Regression, Classification, Time-Series and NLP projects. We will be covering Linear Regression, Logistic Regression, K Nearest Neighbors, Support Vector Machines, Decision Tree, Random Forests, ARIMA, Text Classification and Sentiment Analysis as machine learning algorithms in our course. All projects are going to be end to end so it will be easy to follow what we are doing step by step and I will be giving short explanations for the codes that i write. Main motivation of this course is teaching students how to do projects by theirselves. By taking this course you will be experienced in data science projects and you can apply the codes by yourself in order to build yor own project. Building projects is one of the most important ways to get into and learn Data Science. Thanks for reading, if you are interested in Data Science lets meet in the first lesson.
Embark on a Journey into the World of Data Science and Machine Learning!Welcome to the Mastering Data Science & Machine Learning Fundamentals for Beginners course, a comprehensive and illuminating exploration of the captivating realms of Data Science and Machine Learning!In today's rapidly evolving landscape, Data Science and Machine Learning are not mere buzzwords; they are the driving forces behind innovation in diverse domains, including IT, security, marketing, automation, and healthcare. These technologies underpin the very foundations of modern conveniences, from email spam filters and efficient Google searches to personalized advertisements, precise weather forecasts, and uncanny sports predictions. This course is your gateway to understanding the magic behind these advancements. Designed with students and learners in mind, this course aims to demystify complex machine learning algorithms, statistics, and mathematics. It caters to those curious minds eager to solve real-world problems using the power of machine learning. Starting with the fundamentals, the course progressively deepens your understanding of a vast array of machine learning and data science concepts. No prior knowledge or experience is required to embark on this enriching learning journey. This course not only simplifies intricate machine learning concepts but also provides hands-on guidance on implementing them successfully. Our esteemed instructors, experts in data science and AI, are your trusted guides throughout this course. They are committed to making each concept crystal clear, steering away from confusing mathematical notations and jargon, and ensuring that everything is explained in plain English. Here's a glimpse of what you'll delve into:Mastering Machine Learning FundamentalsDistinguishing between Supervised and Unsupervised L
Unlock the Power of AI: From Beginner to Advanced Machine Learning & Deep Learning ProjectsAre you ready to dive into the world of Artificial Intelligence and master Machine Learning and Deep Learning? Whether you're just starting or want to expand your AI skills, this comprehensive course is designed to guide you through hands-on projects that you can use to showcase your abilities in the real world.Key Highlights of the Course:Hands-On, Project-Based Learning: This is not just a theory-heavy course. You’ll be actively building and deploying AI models that solve real-world problems. Each module introduces a new project, ensuring you gain practical experience while learning.Perfect for Beginners to Experts: Start with the basics and move towards advanced concepts at your own pace. Whether you're new to AI or looking to deepen your knowledge, this course will meet you where you are and help you grow.Practical AI Applications: Learn to apply AI in fields like image classification, natural language processing (NLP), recommendation systems, and more, giving you a diverse skillset that can be applied to various industries.Master Deep Learning: Learn cutting-edge techniques like neural networks, CNNs (Convolutional Neural Networks), and RNNs (Recurrent Neural Networks) to handle complex tasks, opening up exciting opportunities in AI development.Deployment & Scalability: Learn to take your models from development to deployment. Understand how to use cloud platforms and scaling strategies to make your AI solutions accessible and efficient.Collaborative Learning: Engage with fellow learners, share your progress, and collaborate on projects, creating a supportive and dynamic learning environment.Expert Mentorship:<
You’ve just stumbled upon the most complete, in-depth Neural Networks for Regression course online.Whether you want to:- build the skills you need to get your first data science job- move to a more senior software developer position- become a computer scientist mastering in data science- or just learn Neural Networks to be able to create your own projects quickly....this complete Neural Networks for Regression Masterclass is the course you need to do all of this, and more.This course is designed to give you the Neural Network skills you need to become a data science expert. By the end of the course, you will understand the Multilayer Perceptron Neural Networks for Regression method extremely well and be able to apply them in your own data science projects and be productive as a computer scientist and developer.What makes this course a bestseller?Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.Like you, they were tired of low-quality lessons, poorly explained topics, and confusing info presented in the wrong way. That’s why so many find success in this complete Neural Networks for Regression course. It’s designed with simplicity and seamless progression in mind through its content.This course assumes no previous data science experience and takes you from absolute beginner core concepts. You will learn the core dimensionality reduction skills and master the Multilayer Perceptron (MLP) technique. It's a one-stop shop to learn Multilayer Networks. If you want to go beyond the core content you can do so at any time.What if I have questions?As if this course wasn’t complete enough, I
Are you preparing for a career in Data Science or Machine Learning? Mastering the technical skills is crucial, but excelling in interviews requires more than just technical knowledge. Our course, "Data Science and Machine Learning: Top Interview Questions," equips you with the essential insights and strategies to ace your interviews with confidence.In this comprehensive course, we delve into the core concepts and practical techniques that are frequently tested in interviews for data science and machine learning roles. From feature engineering and model evaluation to unsupervised learning and ensemble methods, we cover a wide range of topics essential for success in interviews.Through a series of curated hands-on exercises, you will gain proficiency in:Crafting effective feature engineering and selection strategies to optimize model performance.Understanding various performance metrics and validation techniques to assess model accuracy and generalization.Exploring unsupervised learning algorithms and ensemble methods for tackling complex data problems.Leveraging cross-validation strategies to ensure robustness and reliability of your machine learning models.Moreover, our course goes beyond technical skills to offer invaluable interview insights, tips, and best practices. You'll learn how to articulate your thought process, communicate your solutions effectively, and tackle interview questions with clarity and confidence.Whether you're a seasoned professional or a beginner in the field, "Data Science and Machine Learning: Top Interview Questions" provides you with the knowledge and skills needed to excel in your next interview and kickstart your career in data science and machine learning. Enroll now and take the next step towards your dream job!
Machine learning and Deep Learning have been gaining immense traction lately, but until now JavaScript developers have not been able to take advantage of it due to the steep learning curve involved in learning a new language. Here comes a browser based JavaScript library, TensorFlow.js to your rescue using which you can train and deploy machine learning models entirely in the browser. If you’re a JavaScript developer who wants to enter the field ML and DL using TensorFlow.js, then this course is for you.This course takes a step by step approach to teach you how to use JavaScript library, TensorFlow.js for performing machine learning and deep learning on a day-to-day basis. Beginning with an introduction to machine learning, you will learn how to create machine learning models, neural networks, and deep learning models with practical projects. You will then learn how to include a pre-trained model into your own web application to detect human emotions based on pictures and voices. You will also learn how to modify a pre-trained model to train the emotional detector from scratch using your own data.Towards the end of this course, you will be able to implement Machine Learning and Deep Learning for your own projects using JavaScript and the TensorFlow.js library.Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Arish Ali started his machine learning journey 5 years ago by winning an all-India machine learning competition conducted by IISC and Microsoft. He was a data scientist at Mu Sigma, one of the biggest analytics firms in India. He has worked on some cutting-edge problems involved in multi-touch attribution modeling, market mix modeling, and Deep Neural Networks. He has also been an Adjunct faculty for Predictive Business Analytics at the Bridge School of Management, which along with Northwestern University (SPS) offers a course in
UPDATES NOVEMBER 20252026 Version of the course released with all code up to date.OpenAI Responses Endpoint and GPT-5 implemented across the sections.New no-code RAG with Flowise.New Project with Streamlit.UPDATES JUNE 2025Launched 2 sections: Image Generation with OpenAI and Reasoning ModelsMCP is now live!UPDATES MAY 2025Launch of 2 new sections: RAG with OpenAI File Search and RAGASMinor video remakes due to mistakes.UPDATES APRIL 2025:Remake of 3 sections: Retrieval Fundamentals, Generative Fundaments and Introduction to RAGAdded Knowledge Graphs with Light RAGUPDATES DECEMBER 2024:Fine Tuning OpenAI GPT-4oPython Crash Course + Self-assessmentUPDATES NOVEMBER 2024:CrewAI and CrewAI Capstone Project launchedThe section on OpenAI API for Text and Images is live + OpenAI API Capstone ProjectUPDATES OCTOBER 2024:OpenAI Swarm is liveAgentic RAG is liveMultimodal RAG Project is liveUnlock the Power of RAG, AI Agents, and Generative AI with Python and OpenAI in 2026!Welcome to "RAG, AI Agents, and Generative AI with Python and OpenAI 2026"—the ultimate course to master Retrieval-Augmented Generation (RAG), AI Agents, and Generative AI using Python and OpenAI's cutting-edge technologies. If you aspire to become a leader in artificial intelligence, machine learning, and natural language processing, this is the course you've been waiting for!<s
Intro to deep learning using Keras. Build neural networks for image classification and regression.
This course offers a beginner-friendly introduction to the core concepts of computer vision using Python. You will learn to manipulate images, detect features like faces and eyes, and perform object recognition with popular libraries like OpenCV and Dlib.
This LinkedIn Learning course provides a practical introduction to hypothesis testing for data science. You will learn about the different types of hypothesis tests and how to apply them to real-world data. The course includes hands-on exercises using Python.
Fast-paced introduction to machine learning using TensorFlow. Covers essential ML concepts with hands-on exercises and real-world examples.
A concise, beginner-friendly course that provides a quick dive into the essentials of crafting effective prompts for various generative AI systems. It's packed with insights despite its short duration.
This Course Cover Topics such as Python Basic Concepts, Python Advance Concepts, Numpy Library , Scipy Library , Pandas Library, Matplotlib Library, Seaborn Library, Plotlypy Library, Introduction to Data Science and steps to start Project in Data Science, Case Studies of Data Science and Machine Learning Algorithms such as Linear, Logistic, SVM, NLPThis is best course for any one who wants to start career in data science. with machine Learning.Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.The course provides path to start career in Data Analysis. Importance of Data, Collection of Data with Case Study is covered. Machine Learning Types such as Supervise Learning, Unsupervised Learning, are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered. This Course will design to understand Machine Learning Algorithms with case Studies using Scikit Learn Library. The Machine Learning Algorithms such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies
85% of data science problems are solved using exploratory data analysis (EDA), visualization, regression (linear & logistic). So naturally, 85% of the interview questions come from these topics as well.This concise course, created by UNP, focuses on what matter most. This course will help you create a solid foundation of the essential topics of data science. With this solid foundation, you will go a long way, understand any method easily, and create your own predictive analytics models.At the end of this course, you will be able to:independently build machine learning and predictive analytics modelsconfidently appear for exploratory data analysis, foundational data science, python interviews demonstrate mastery in exploratory data science and pythondemonstrate mastery in logistic and linear regression, the workhorses of data scienceThis course is designed to get students on board with data science and make them ready to solve industry problems. This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications. Special emphasis is given to regression analysis. Linear and logistic regression is still the workhorse of data science. These two topics are the most basic machine learning techniques that everyone should understand very well. In addition, concepts of overfitting, regularization etc., are discussed in detail. These fundamental understandings are crucial as these can be applied to almost every machine learning method. This course also provides an understanding of the industry standards, best practices for formulating, applying and maintaining data-driven solutions. It starts with a basic explanation of Machine Learning concepts and how to set up your environment. Next, data wrangling and EDA with Pandas are discussed with hands-on
Welcome to the Comprehensive Generative AI Course, your gateway to mastering the exciting world of Generative Artificial Intelligence. With this course, you'll gain the knowledge and skills needed to excel in this rapidly growing field which is expected to be valued at $100 billion over the next years.With multiple hours of content, world class slides and resources, this course is the most detailed Generative AI course you will find. Even if you have zero programming experience, this course will take you from beginner to mastery. Here's why:The course is taught by a PhD in computer science with multiple publications who has taught in multiple universities all over the world for years. The course has been created to be 2023 ready and you'll be learning the latest tools and technologies used at large companies such as OpenAI.This course doesn't cut any corners, there are beautiful detailed presentations, assignments, projects, downloadable resources, articles, and so much more. The curriculum was developed over years while the instructor taught at the university level, with comprehensive student testing and feedback.We've taught thousands of students how to code and many have gone on to change their lives by becoming professional developers or starting their own tech startup.The course is constantly updated with new content, with new projects and modules determined by students - that's you!We'll take you step-by-step through engaging video tutorials and teach you everything you need to know to understand Generative AI and succeed in the industry. The course includes multiple hours of HD video tutorials and builds your knowledge while working on actual a
Are you ready to dive into the fascinating world of object detection using deep learning? In our comprehensive course "Deep Learning for Object Detection with Python and PyTorch", we will guide you through the essential concepts and techniques required to detect, classify, and locate objects in images. Object Detection has wide range of potential real life application in many fields. Object detection is used for autonomous vehicles to perceive and understand their surroundings. It helps in detecting and tracking pedestrians, vehicles, traffic signs, traffic lights, and other objects on the road. Object Detection is used for surveillance and security using drones to identify and track suspicious activities, intruders, and objects of interest. Object Detection is used for traffic monitoring, helmet and license plate detection, player tracking, defect detection, industrial usage and much more.With the powerful combination of Python programming and the PyTorch deep learning framework, you'll explore state-of-the-art algorithms and architectures like R-CNN, Fast RCNN and Faster R-CNN. Throughout the course, you'll gain a solid understanding of Convolutional Neural Networks (CNNs) and their role in Object Detection. You'll learn how to leverage pre-trained models, fine-tune them for Object Detection using Detectron2 Library developed by by Facebook AI Research (FAIR).The course covers the complete pipeline with hands-on experience of Object Detection using Deep Learning with Python and PyTorch as follows:Course BreakDown:Learn Object Detection with Python and Pytorch CodingLearn Object Detection using Deep Learning ModelsIntroduction to Convolutional Neural Networks (CNN)Learn RCNN, Fast RCNN, Faster RCNN, Mask RCNN and YOLO8, YOLO11 ArchitecturesPerform Object Detection with Fast RCNN and Faster RCNNPerform Real-time Video Object Detection with YOLOv8 and YOLO11</
Mastering LoRA Fine-Tuning on Llama 1.1B with the Guanaco Chat Dataset: Training on Consumer GPUsUnleash the potential of Low-Rank Adaptation (LoRA) for efficient AI model fine-tuning with our groundbreaking Udemy course. Designed for forward-thinking data scientists, machine learning engineers, and software engineers, this course guides you through the process of LoRA fine-tuning applied to the cutting-edge Llama 1.1B model, utilizing the diverse Guanaco chat dataset. LoRA’s revolutionary approach enables the customization of large language models on consumer-grade GPUs, democratizing access to advanced AI technology by optimizing memory usage and computational efficiency.Dive deep into the practical application of LoRA fine-tuning within the HuggingFace Transformers framework, leveraging its Parameter-Efficient Fine-Tuning Library alongside the intuitive HuggingFace Trainer. This combination not only streamlines the fine-tuning process, but also significantly enhances learning efficiency and model performance on datasets.What You Will Learn:Introduction to LoRA Fine-Tuning: Grasp the fundamentals of Low-Rank Adaptation and its pivotal role in advancing AI model personalization and efficiency.Hands-On with Llama 1.1B and Guanaco Chat Dataset: Experience direct interaction with the Llama 1.1B model and Guanaco chat dataset, preparing you for real-world application of LoRA fine-tuning.Efficient Training on Consumer GPUs: Explore the transformational capability of LoRA to fine-tune large language models on consumer hardware, emphasizing its low memory footprint and computational advantages.Integration with HuggingFace Transformers: Master the use of the HuggingFace Parameter-Efficient Fine-Tuning Library and the HuggingFace Trainer for streamlined and effective model adaptation.Insightful Analysis of the L
This complete Generative AI course takes you from beginner to advanced with hands-on projects, real-world applications, and career-ready skills. You’ll learn the foundations of Generative AI, explore Large Language Models (LLMs), master frameworks like LangChain, LlamaIndex, CrewAI, and PydanticAI, and deploy your own AI solutions on the cloud. The course is tailored to equip you with both the knowledge and practical experience required to step into a Generative AI Engineer role.Each section includes quizzes & coding exercises to help you test your knowledge and reinforce your skills.What you’ll learn in each section1. Introduction – Get started with the course, understand what you will learn & set up Python environments (Colab, Jupyter, PyCharm).2. Generative AI – Foundation – Understand AI vs ML vs DL vs GenAI, dive into Large Language Models, and learn the Transformer architecture.3. Accessing LLMs in Python – Use OpenAI, Gemini, Groq, and Ollama LLMs, and connect them through LangChain and LlamaIndex.4. Prompt Engineering – Explore prompt templates, zero-shot, and few-shot prompting to effectively interact with LLMs.5. Building GenAI Chatbots – Build and deploy chatbots step by step using LangChain, LlamaIndex, Streamlit UI, and Streamlit Cloud.6. Retrieval-Augmented Generation (RAG) – Understand RAG, build RAG pipelines with LangChain and LlamaIndex, and create a PDF Q&A bot.7. AI Agents – Learn what AI agents are and build agents with PydanticAI, AutoGen, and CrewAI for multi-agent workflows.8. LLM Deployment – Deploy open-source LLMs with Ollama, Docker, and vLLM, and set them up on AWS EC2 for real-world usage.
Learn about Computer Vision, one of the most exciting fields in Machine Learning, Artificial Intelligence and Computer Science.
A beginner-level course that introduces unsupervised learning, clustering, and Principal Component Analysis (PCA). It covers the fundamentals of clustering to group data points and find patterns, and how PCA aids in dimensionality reduction.
This beginner-level course covers the basics of speech recognition, including ASR systems and acoustic models. It explores the principles, applications, and challenges of speech recognition, covering topics like speech signal processing, language modeling, and deep learning techniques.
The demand for Data Scientists is immense. In this course, you'll learn how you can play a part in fulfilling this demand and build a long, successful career for yourself.The #1 goal of this course is clear: give you all the skills you need to be a Data Scientist who could start the job tomorrow... within 6 weeks.With so much ground to cover, we've stripped out the fluff and geared the lessons to focus 100% on preparing you as a Data Scientist. You’ll discover:The structured path for rapidly acquiring Data Science expertiseHow to build your ability in statistics to help interpret and analyse data more effectivelyHow to perform visualizations using one of the industry's most popular toolsHow to apply machine learning algorithms with Python to solve real world problemsWhy the cloud is important for Data Scientists and how to use itAlong with much more. You'll pick up all the core concepts that veteran Data Scientists understand intimately. Use common industry-wide tools like SQL, Tableau and Python to tackle problems. And get guidance on how to launch your own Data Science projects.In fact, it might seem like too much at first. And there is a lot of content, exercises, study and challenges to get through. But with the right attitude, becoming a Data Scientist this quickly IS possible!Once you've finished Introduction to Data Science, you’ll be ready for an incredible career in a field that's expanding faster than almost anything else in the world.Complete this course, master the principles, and join the ranks of Data Scientists all around the world.
A foundational course for beginners that provides a comprehensive overview of Generative AI concepts, applications, challenges, and opportunities.
An introductory course that provides an interactive environment to learn the basics of CUDA. It covers launching parallel CUDA Kernels, organizing thread execution, managing memory between the CPU and GPU, and profiling CUDA code to observe performance gains.
This course is the best for mastering the Data Science and Machine Learning from basics. If you are new to Data Science and Machine Learning, This course will help you to learn everything from Basics. This course is designed as a comprehensive and accessible introduction to two of the most transformative fields in the modern digital era. Tailored specifically for those with little to no prior experience, this course aims to demystify the core concepts of data science and machine learning while building a strong foundation for future exploration. Whether you're a student, professional, or enthusiast looking to transition into the tech industry, this course provides the essential knowledge and practical skills to get started.The course begins with a clear overview of what data science is, covering the data lifecycle—from collection and cleaning to analysis and visualization. You are introduced to key tools used in the industry, including Python programming, Jupyter notebooks, and essential libraries like Pandas, NumPy, and Matplotlib. With a hands-on approach, students engage in real-world data manipulation exercises that emphasize clarity and intuition over complexity.By the end of the course, You will have a solid understanding of how data science and machine learning work together to extract insights and drive innovation. They will be equipped with the confidence and skills to explore more advanced topics or pursue further studies in data analysis, machine learning, or artificial intelligence. This beginner-friendly course lays the groundwork for a successful journey into the exciting world of data science, empowering learners to unlock the value hidden in data and make informed, intelligent decisions.
Ready to master machine learning in Python and launch your career in data science? This hands-on, comprehensive course is the definitive guide to becoming a skilled practitioner, taking you from the fundamentals of Scikit-learn to building powerful, real-world AI models.You'll gain a deep understanding of Scikit-learn, Python's most essential and widely used machine learning library. By focusing on practical application, you will not only learn the algorithms but also how to implement the full data science workflow—a critical skill for employers.Master the Complete Data Science and Machine Learning WorkflowThis masterclass will teach you to:Prepare and Preprocess complex, real-world datasets using Python (Pandas & NumPy) and the integrated tools within Scikit-learn.Build Powerful Models using core Machine Learning algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVMs).Optimize Performance with advanced techniques like Regularization, Cross-Validation, and Principal Component Analysis (PCA) for Dimensionality Reduction.Apply both Supervised and Unsupervised Learning to solve diverse business problems in data science.Understand the AI Landscape by covering the basics of Neural Networks and their role in Deep Learning.Work through short coding exercises and large, project-style assignments, mirroring the daily work of a professional data scientist.Why Learn Machine Learning with Us?We're
Ready to unlock the full potential of AI? This demo-focused course teaches you how to craft powerful prompts for Grok and ChatGPT, transforming how you create content, write emails, generate images, and streamline workflows. Perfect for beginners and professionals, no prior AI experience is needed—just simple prompts to achieve professional-grade results.This course empowers you to harness Grok and ChatGPT for professional tasks like email writing, content creation, and image generation, plus personal projects as a bonus, saving time and boosting productivity.What You’ll LearnWith Grok and ChatGPT, you can:Craft Professional Emails: Write sales pitches, job applications, or customer responses in seconds, tailored to any audience or style.Create Compelling Content: Generate blog posts, social media content, or ad copy for marketing, education, or creative projects.Design Stunning Visuals: Use tools like Stable Diffusion, Grok, Midjourney, Wombo or DALL·E to create vivid images with precise prompts.Automate Workflows: Streamline repetitive tasks like reports, summaries, or scheduling with clear, structured prompts.Boost Creativity: Produce stories, lesson plans, or innovative solutions with AI-driven precision.Course HighlightsDemo-Driven Learning: See Grok and ChatGPT in action with real-world examples across email, content, and image creation.Comprehensive Prompt Engineering: Master prompt components (instruction, context, constraints, examples) and types (open-ended, role-based, few-shot, zero-shot).Avoid Common Pitfalls: Learn to eliminate ambiguity, overloading, and vague prompts
This course provides a comprehensive introduction to data wrangling with the Pandas library in Python, covering essential data cleaning and transformation techniques.
Welcome to your transformative journey into the world of artificial intelligence and deep learning! This isn't just another course – it's your comprehensive AI education blueprint that delivers the equivalent content of five premium courses bundled into one power-packed learning experience. After six months of intensive research, we've created a program that will transform you from a complete beginner into a confident AI practitioner.What You'll LearnMaster fundamental principles of machine learning and advance to transformer models, attention mechanisms, and generative AIBuild your first neural networks using PyTorch and TensorFlowExplore natural language processing (NLP) with GPT-4, Claude, and other large language models (LLMs)Develop AI agents using LangChain that can reason, plan, and execute complex tasksCreate Retrieval-Augmented Generation (RAG) systems with vector databases and embeddingsMaster prompt engineering techniques for optimal AI resultsImplement computer vision applications using convolutional neural networks (CNNs)Apply reinforcement learning principles to create self-improving AI agentsDesign AI automation strategies that streamline workflows and reduce costsUnderstand AI ethics and responsible development practicesLearn model fine-tuning techniques for specific domainsDeploy AI solutions using AWS, Google Cloud
DescriptionTake the next step in your cloud-powered AI and machine learning journey! Whether you're an aspiring data scientist, ML engineer, developer, or business leader, this course will equip you with the skills to harness AWS for scalable, real-world data science and machine learning solutions. Learn how services like SageMaker, Glue, Redshift, and QuickSight are transforming industries through data-driven intelligence, automation, and predictive analytics.Guided by hands-on projects and real-world use cases, you will:• Master foundational data science workflows and machine learning principles using AWS cloud services.• Gain hands-on experience managing data with S3, Redshift, Glue, and building models with AWS SageMaker.• Learn to train, optimize, and deploy ML models at scale using advanced tools like AutoML, hyperparameter tuning, and deep learning frameworks.• Explore industry applications in e-commerce, finance, healthcare, and manufacturing using AWS AI/ML solutions.• Understand best practices for cost management, security, and automation in cloud-based data science projects.• Position yourself for a competitive advantage by building in-demand skills at the intersection of cloud computing, AI, and machine learning.The Frameworks of the Course· Engaging video lectures, case studies, projects, downloadable resources, and interactive exercises— designed to help you deeply understand how to leverage AWS for data science and machine learning applications.· The course includes industry-specific case studies, cloud-native tools, reference guides, quizzes, self-paced assessments, and hands-on labs to strengthen your ability to build, manage, and deploy ML models using AWS services.· In the first part of the course, you’ll learn the basics of data science, machine learning, and how AWS enables scalable cloud-based solutions.· In
Learn AI Code Generation with GitHub Copilot
Artificial Intelligence is transforming the world — and AI agents are at the heart of that revolution. From virtual assistants and research bots to automated problem-solvers, agents are the next leap beyond simple chatbots. In this hands-on course, you’ll learn step-by-step how to build your own working AI agent using LangChain, Python, and the OpenAI API — no advanced technical experience required.We’ll start from the very beginning. You’ll understand what AI really means, how agents differ from chatbots, and where they’re being used in the real world. Then, you’ll dive into the practical side — setting up your Python environment, installing key tools, and connecting APIs like OpenAI and SerpAPI to bring intelligence and search power to your creations.Once your setup is complete, we’ll explore the core concepts of LangChain — including chains, agents, prompts, and tools. You’ll learn how to create prompt templates, build your own tools, and finally, connect them all to create an AI agent that can think, search, and respond intelligently. By the end, you’ll not only understand how LangChain works, but also how to build, test, and customize your own AI-powered assistants for real-world use.Whether you’re an aspiring developer, a curious beginner, or an entrepreneur exploring the future of automation, this course gives you the practical foundation you need to start building with confidence.By completing this course, you’ll be able to: Understand the fundamentals of AI and LangChain Set up your development environment from scratch Build and test working AI agents using Python and OpenAI Extend your agents with real-world tools and APIsJoin today, and take your first step toward building intelligent AI agents that can reason, search, and act — all powered by LangChain.
Section 1 Introduction to the Course: In this section we'll learn about the basics of prompt engineering, simple prompt starters, practical everyday prompts and much more about Prompt engineering i.e Is the art of designing effective prompts to guide AI in generating accurate and useful responses.Section 2 About Prompt Revisions (Prompt Engineering): Prompt revisions refine instructions given to AI for better results. This involves adding context, clarifying ambiguity, using precise language, and iterating until the outcome meets expectations.Section 3 General Prompt Frameworks (Prompt Engineering): In this section we'll learn about general prompt frameworks i.e Structured prompts with clear instructions, context, input, and output ensure accurate AI responses.Section 4 Learn to Generate Image using AI: Learn to generate image with the help of AI and Image prompt book and prompt engineering.Section 5 Generate Business Ideas using ChatGPT: ChatGPT can help brainstorm business ideas by analyzing trends, identifying gaps, and suggesting innovative solutions based on your interests or market needs.Section 6 Brainstorming Freelancing Ideas using AI (Chat GPT): ChatGPT can help generate freelancing ideas by suggesting in-demand skills, niches, and services based on current trends, allowing you to explore unique opportunities and tailor them to your expertise.Section 7 Improve Health and Wellness using AI (Chat GPT): ChatGPT can provide personalized health tips, fitness plans, and dietary advice, helping you track progress, set goals, and adopt healthy habits based on your individual needs.Section 8 Learn Specific Skills using ChatGPT: Learn specific skills like Seo with AI, How to write blogs, Creating website, Creating an AD, Content writing and much more with the help of Chat Gpt.<stron
Master OCR with Python and OpenCV: Become a Computer Vision ExpertUnlock the Power of Text Extraction with AI & Generative AIThis comprehensive course will equip you with the skills to:Build Cutting-Edge OCR Systems: Go beyond traditional OCR with Python and OpenCV. Learn to leverage the power of Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) to create intelligent and accurate text extraction systems.Master Deep Learning Techniques: Dive into advanced deep learning models like CTPN and EAST for text detection and recognition.Integrate GenAI for Enhanced OCR: Discover how to integrate Generative AI with LLMs and RAG to improve OCR accuracy, extract insights from unstructured text, and automate complex document processing tasks.Apply OCR to Real-World Scenarios: Implement OCR solutions for a variety of applications, including document digitization, invoice processing, and more.Stay Ahead of the Curve: Keep up with the latest advancements in OCR, Computer Vision, LLMs, RAG, and Generative AI.Key Features:Hands-On Projects: Gain practical experience with real-world projects, such as invoice processing, KYC digitization, and business card recognition.Expert Guidance: Learn from experienced instructors who will guide you through every step of the process.In-Depth Coverage: In-Depth Coverage: Explore a wide range of topics, from fundamental image processing and deep learning to advanced LLM and RAG techniques.Dedicated Support: Get 24/7 support from our team of experts.Flexible Le
Lecture 1: IntroductionHere you will find a short introduction to the course. We outline the objectives, structure, and practical outcomes. This sets the stage for a hands-on experience in machine learning with EEG signals.Lecture 2: Connect to Google ColabThis chapter provides a step-by-step guide on how to connect to and work in Google Colab. You’ll learn how to set up your environment, install required libraries, and ensure you are ready to run the code examples provided throughout the course.Lecture 3: Hardware for Brain-Computer InterfaceThis chapter covers the essential hardware used in EEG-based brain-computer interfaces. Lecture 4: Data EvaluationWe dive into evaluating the quality of your EEG data. This chapter explores techniques to inspect, clean, and annotate EEG recordings, ensuring that your data is reliable before moving forward with analysis or machine learning tasks.Lecture 5: Prepare the DatasetLearn how to transform raw EEG signals into structured datasets suitable for machine learning. This chapter includes labeling, segmenting, and feature extraction techniques—critical steps for successful model training and testing.Lecture 6: Introduction to DLIn this chapter, we introduce the fundamentals of deep learning and explain why Keras is a suitable library for working with EEG data. You’ll gain a basic understanding of deep learning concepts, how they apply to EEG signal processing, and where to find more information about Keras and its capabilities. This sets the foundation for implementing neural networks in upcoming lectures.Lecture 7. Convolutional Neural Networks (CNNs) for EEGThis chapter introduces convolutional neural networks (CNNs) and their application to EEG signal processing. You’ll learn the theory behind CNNs, how they are used for automatic feature extraction, and how to i
Welcome to the exciting world of Matrix Calculus, a fundamental tool for understanding and solving problems in machine learning and data science. In this course, we will dive into the powerful mathematics that underpin many of the algorithms and techniques used in these fields. By the end of this course, you'll have the knowledge and skills to navigate the complex landscape of derivatives, gradients, and optimizations involving matrices.Course Objectives:Understand the basics of matrix calculus, linear and quadratic forms, and their derivatives.Learn how to utilize the famous Matrix Cookbook for a wide range of matrix calculus operations.Gain proficiency in optimization techniques like gradient descent and Newton's method in one and multiple dimensions.Apply the concepts learned to real-world problems in machine learning and data science, with hands-on exercises and Python code examples.Why Matrix Calculus? Matrix calculus is the language of machine learning and data science. In these fields, we often work with high-dimensional data, making matrices and their derivatives a natural representation for our problems. Understanding matrix calculus is crucial for developing and analyzing algorithms, building predictive models, and making sense of the vast amounts of data at our disposal.Section 1: Linear and Quadratic Forms In the first part of the course, we'll explore the basics of linear and quadratic forms, and their derivatives. The linear form appears in all of the most fundamental and popular machine learning models, including linear regression, logistic regression, support vector machine (SVM), and deep neural networks. We will also dive into quadratic forms, which are fundamental to understanding optimization problems, which appear in regression, portfolio optimization in finance, signal processing, and control theory.The Mat
In today's data-driven world, the ability to harness and interpret data is not just a valuable skill but a crucial advantage. Whether you're an aspiring data scientist, a seasoned professional looking to expand your skill set, or an entrepreneur aiming to leverage data for strategic decisions, our comprehensive course on data science offers a transformative learning experience.Course OverviewOur course begins with a foundational exploration of data science, introducing you to its principles and importance in various industries. You'll delve into the distinctions between data science, data engineering, and data analysis, gaining a clear understanding of their respective roles and applications. Through real-world case studies and examples, you'll discover how data science drives innovation and impacts decision-making processes across different sectors.Essential Tools and TechnologiesTo equip you with the tools needed for effective data analysis, the course covers essential programming languages such as Python . Whether you're manipulating data with Pandas, performing numerical operations with NumPy, or creating insightful visualizations with Matplotlib and Seaborn, you'll develop a versatile skill set that forms the backbone of data science projects.Practical Skills DevelopmentA significant focus of the course is hands-on learning. You'll gain practical experience in gathering, cleaning, and analyzing data from diverse sources. Through interactive exercises and projects, you'll hone your ability to transform raw data into actionable insights that drive business decisions.Environment Setup and Best PracticesNavigating the data science environment can be daunting, especially for beginners. That's why we guide you through the setup of Python and Jupyter Notebook on both Windows and macOS, ensuring you're equipped with the right tools from the start. You'll learn to create and manage virtual environments, enhancing your ability to work efficiently and mai
Unlock the Power of Data Science SkillsIn today's data-driven world, the ability to harness and interpret data is not just a valuable skill but a crucial advantage. Whether you're an aspiring data scientist, a seasoned professional looking to expand your skill set, or an entrepreneur aiming to leverage data for strategic decisions, our comprehensive course on data science offers a transformative learning experience.Course OverviewOur course begins with a foundational exploration of data science, introducing you to its principles and importance in various industries. You'll delve into the distinctions between data science, data engineering, and data analysis, gaining a clear understanding of their respective roles and applications. Through real-world case studies and examples, you'll discover how data science drives innovation and impacts decision-making processes across different sectors.Essential Tools and TechnologiesTo equip you with the tools needed for effective data analysis, the course covers essential programming languages such as Python and R. Whether you're manipulating data with Pandas, performing numerical operations with NumPy, or creating insightful visualizations with Matplotlib and Seaborn, you'll develop a versatile skill set that forms the backbone of data science projects.Practical Skills DevelopmentA significant focus of the course is hands-on learning. You'll gain practical experience in gathering, cleaning, and analyzing data from diverse sources. You'll hone your ability to transform raw data into actionable insights that drive business decisions.Environment Setup and Best PracticesNavigating the data science environment can be daunting, especially for beginners. That's why we guide you through the setup of Python and Jupyter Notebook on both Windows and macOS, ensuring you're equipped with the right tools from the start. You'll learn to create and manage virtual environments, enhancing your abili
Master LangChain, OpenAI, Llama, DeepSeek and Hugging Face. Learn to Create hands-on generative LLM-powered applications with LangChain. Create powerful web-based front-ends for your LLM Application using Streamlit.By the end of this course, you will have a solid understanding of the fundamentals of LangChain OpenAI, Llama, DeepSeek and HuggingFace. You'll also be able to create modern front-ends using Streamlit in Python.Dive into hands-on projects that will shape your expertise, including:Project 1: Create a Simple Chatbot with Llama 2 and LangChainProject 2: PDF Chat App (GUI) | ChatGPT for Your PDF File - Streamlit Application to chat with your PDF file using LangChain and OpenAI.Project 3: YouTube Script Writing App - Effortlessly create title and script for the YouTube video using LangChain and OpenAIProject 4: MCQ Quiz Creator App - Seamlessly create multiple-choice quizzes for your students using LangChain and OpenAI/ Hugging FaceProject 5: Chat with Multiple PDF Documents | Streamlit Application- Chat with your PDF files using LangChain and OpenAI.Project 6: Support Chat Bot For Your Website - Helps your visitors/customers to find the relevant data or blog links that can be useful to them.Project 7: YouTube Video Summarizer - YouTube Video Summarizer, powered by the dynamic duo of LangChain and OpenAI! In this groundbreaking tool, we have harnessed the cutting-edge capabilities of language processing technology to transform the way you consume YouTube content.Project 8: Summarize PDF Using LangChain, OpenAI and Gradio: Summarize PDF files using Lang Chain and OpenAI and create a sharable web interface using GradioProject 9: PrivateGPT- Chat with your Files Offline and FreeP
Updated videos with new and improved slides. Fixed all the voice issues. Hope you like the course and please give feedback!Unlock the Power of Amazon Bedrock to Build AI-Powered ApplicationsWelcome to Mastering Amazon Bedrock, a comprehensive course designed to help you harness the power of AWS Bedrock’s tools and services. Whether you're a beginner or an experienced developer, this course will take you step-by-step through concepts, configurations, and hands-on exercises that showcase the potential of AWS Bedrock in building intelligent applications.What You’ll Learn:Knowledge Bases (KB): Dive deep into the concept of vector embeddings and retrieval-augmented generation (RAG), essential for optimizing large-scale AI applications. Learn how to configure Knowledge Bases and integrate them seamlessly with other AWS Bedrock tools using practical examples to solidify your understanding.RAG with Amazon Bedrock - We will use Anthropic Claude Model with OpenSearch Serverless as vector storage to perform the RAG operationsRAG with Open Source - We will also use OpenAI's ChatGPT model with in memory vector storage to perform RAG operationsRetrievers - RAG pattern relies heavily on retrieval. There are many ways to retrieve data for summarization. We will learn and explore about different ways to retrieve the contents. Followed by a hands-on activity AI Agents: Master the configuration of AWS Bedrock agents to streamline AI workflows. Gain hands-on experience in implementing action groups, handling parameters, and orchestrating requests effectively to Knowledge Bases. Understand how agents serve as the backbone of dynamic and intelligent AI interactions. We will cover 2 use cases of AI Agents. Multimodal Nutritional AI Agent - We
Welcome to Supply Chain Analysis with Machine Learning & Neural Network course. This is a comprehensive project based course where you will learn step by step on how to perform complex analysis and visualization on supply chain dataset. This course will be mainly focusing on performing cost optimization, demand forecasting, lead time efficiency, risk management, and order quantity optimization. We will be utilizing two different models, those are LightGBM which is a machine learning model and RNN which stands for Recurrent Neural Networks. Regarding programming language, we are going to use Python alongside several libraries like Pandas for performing data modelling, Numpy for performing complex calculations, Matplotlib for visualizing the data, and Scikit-learn for implementing the machine learning models.Meanwhile, for the data, we are going to download the supply chain dataset from Kaggle. In the introduction session, you will learn basic fundamentals of supply chain analytics, such as getting to know its key objectives, getting to know models that will be used, and challenges that we commonly faced when it comes to analyzing supply chain data for example demand volatility and data integration. Then, you will continue by learning the basic mathematics and logics behind price and order quantity optimization where you will be guided step by step on how to solve a basic case study using economic order quantity equation. This session was designed to prepare your knowledge and understanding about order quantity optimization before implementing this concept to your code in the project. Afterward, you will learn about several different factors that can potentially cause supply chain disruption, such as natural disaster, economic volatility, and supplier issues. Once you’ve learnt all necessary knowledge about supply chain analytics, we will start the project. Firstly, you will be guided step by step on how to set up Google Colab IDE, then, you will also learn how to
Whether you're a manual tester looking to get into automation or a QA engineer curious about how AI can help, but not replace you, this course is for you.In this hands-on course, you'll learn how to use tools like ChatGPT and GitHub Copilot to boost your productivity, reduce repetitive work, and accelerate your testing workflow. No deep coding experience required! We’ll guide you through everything step by step.We'll start with the basics: what AI is (and isn’t), how it fits into QA, and how to prompt tools like ChatGPT to generate test cases, write automation scripts, and even help with bug reporting. From there, you’ll build confidence using GitHub Copilot to generate and refactor real automated tests in Playwright, while learning how to review and improve what the AI gives you.This isn’t just “watch a tool do stuff.” You’ll actually learn how to guide AI tools like a QA pro, because you still drive the logic. AI just helps you get there faster.Tools We’ll Use:ChatGPT (Plus version recommended)GitHub Copilot in Visual Studio Code (Pro version recommended)Playwright (for test automation examples)JavaScript (beginner-friendly)By the end of this course, you’ll understand how to confidently use modern AI tools to make your QA work faster, smarter, and a lot more fun.
Are you ready to learn how to build powerful and AI-supported chatbots from scratch?there are a lot of courses out there that teach you how to develop chatbots. So what makes this course DIFFERENT?We're NOT going to use any cloud-based chatbot solutions like Dialogflow, IBM Watson, or Microsoft Azure. Instead, we'll be focusing on free and open-source technologies that are just as robust and powerful.We're NOT just going to talk only about the basics of chatbot development. We’re going to dive deeply into this world.This course is full of project-based tutorials. A lot of techniques will be derived via developing a set of chatbot projectsChatbots are everywhere and are becoming an increasingly important part of our daily lives. They're used for a wide range of applications, from customer service to online shopping, and they're only getting more advanced and sophisticated.In the course, we delve into the different types of chatbots and their use cases, including rule-based chatbots, AI-powered chatbots, and conversational AI. We also cover the various technologies and platforms that are used to build chatbots, such as natural language processing (NLP), machine learning (ML), and chatbot development open-source projects like Botpress, SetFit, GLiNER, Transformers, langChain, fastAPI, Docker, and more.In this course, you will learn:How to Setup Your Development Environment ToolsHow to Install and start your first Botpress projectYou will Understand what the conversation flow studio isDevelop the different types of chatbot response templatesYou will learn how
You want to start developing deep learning solutions, but you do not want to lose time in mathematics and theory?You want to conduct deep learning projects, but do not like the hassle of tedious programming tasks?Do you want an automated process for developing deep learning solutions?This course is then designed for you! Welcome to Deep Learning in Practice, with NO PAIN!This course is the first course on a series of Deep Learning in Practice Courses of Anis Koubaa, namelyDeep Learning in Practice I: Tensorflow 2 Basics and Dataset Design (this course): the student will learn the basics of conducting a classification project using deep neural networks, then he learns about how to design a dataset for industrial-level professional deep learning projects. Deep Learning in Practice II: Transfer Learning and Models Evaluation: the student will learn how to manage complex deep learning projects and develop models using transfer learning using several state-of-the-art CNN algorithms. He will learn how to develop reusable projects and how to compare the results of different deep learning models in an automated manner. Deep Learning in Practice III: Face Recognition. The student will learn how to build a face recognition app in Tensorflow and Keras.Deep Learning in Practice I: Basics and Dataset DesignThere are plenty of courses and tutorials on deep learning. However, some practical skills are challenging to find in this massive bunch of deep learning resources, and that someone would spend a lot of time to get these practical skills.This course fills this gap and provides a series of practical lectures with hands-on projects through which I introduce the best practices that deep learning practitioners have to know to conduct deep learning projects.I have
Artificial Intelligence (AI) revolution is here and TensorFlow 2.0 is finally here to make it happen much faster! TensorFlow 2.0 is Google’s most powerful, recently released open source platform to build and deploy AI models in practice.AI technology is experiencing exponential growth and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab.The course provides students with practical hands-on experience in training Artificial Neural Networks and Convolutional Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab. This course covers several technique in a practical manner, the projects include but not limited to:(1) Train Feed Forward Artificial Neural Networks to perform regression tasks such as sales/revenue predictions and house price predictions(2) Develop Artificial Neural Networks in the medical field to perform classification tasks such as diabetes detection.(3) Train Deep Learning models to perform image classification tasks such as face detection, Fashion classification and traffic sign classification.(4) Develop AI models to perform sentiment analysis and analyze customer reviews.(5) Perform AI models visualization and assess their performance using Tensorboard(6) Deploy AI models in practice using Tensorflow 2.0 ServingThe course is targeted towards students wanting to gain a fundamental understanding of how to build and deploy models in Tensorflow 2.0. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master AI and Deep Learning techn
Welcome to Building Machine Learning & NLP Models for Cyber Security course. This is a comprehensive project based course where you will learn how to build intrusion detection system, predict vulnerability score, and classify cyber threat using machine learning models like Random Forest Classifier, Logistic Regression, MLP Regressor, Decision Tree Regressor, KNN, XGBoost, Naive Bayes, and K Means Clustering. This course is a perfect combination between machine learning and cyber security, making it an ideal opportunity to practice your programming skills while improving your technical knowledge in system security. In the introduction session, you will learn about machine learning and natural language processing applications in cyber security, specifically how it can help to enhance risk management and strengthen overall security. Then, in the next section, we will learn how intrusion detection models work. This section will cover data collections, data preprocessing, feature selection, splitting data into training and testing sets, model selection, model training, detecting intrusion, model evaluation, deployment, and monitoring. Afterward, we will download cyber security datasets from Kaggle, it is a platform that offers many high quality datasets from various sectors. Once everything is all set, then, we will start the project, firstly, we will clean the dataset by removing all missing values and duplicates, after we make sure the data is clean and ready to use, we will start exploratory data analysis, firstly we are going to analyze the relationship between protocol type and intrusion, which will enable us to understand how different communication protocols contribute to intrusion risk, following that, we are also going to analyze intrusion rate for each browser type, which will allow us to uncover potential vulnerabilities associated with specific browsers, then, we are going to calculate the average login attempts and failed logins for both normal and intru
Most LangChain and LangGraph courses are Python-first. This one is built from the ground up for JavaScript & TypeScript engineers who want real, shippable agentic systems—not disconnected demos.You’ll build a sequence of end-to-end projects that mirror how modern teams ship AI features: clean TypeScript code, clear APIs, JSON contracts, LangGraph orchestration, RAG, proper vector stores, and real Next.js frontends wired to real agents.By the end, you’ll know exactly how to go from idea → design → implementation → observability → deployment in the JS ecosystem.Here’s what we’ll cover in Phase 1:Intro & MindsetHow this course works, what it is / isn’t, and how to follow.Choosing models (OpenAI / Gemini / Groq / local) smartly for cost, speed & reliability.How all projects connect into a reusable “agent platform” you can extend.Foundations: LangChain, Agents & FlowModern AI app architecture: UI → orchestration → models → tools → storage.Simple, honest definition of AI agents and real-world use cases.Chains vs agents: when a chain is enough, when an agent is worth it.Where LangChain.js fits, where LangGraph.js fits, and how they work together.JSON-first mindset teaser: why strings lie and schemas save you.Orientation & “Hello Agent” ProjectTS/Node project setup, tsconfig, env patterns, scripts.Multi-provider setup: OpenAI, Gemini, Groq via a single provider factory.First “Hello Agent” function that runs like a clean backend primitive, not a toy script.LLM Fundamentals: JS
Welcome to the forefront of artificial intelligence with our groundbreaking course on Generative AI (GenAI), the OpenAI API, DeepSeek, and ChatGPT. With ChatGPT and DeepSeek, you'll learn how to build with the world's most advanced Large Language Models (LLMs). This course is a must-have if you want to know how to use this cutting-edge technology for your business and work projects.This course contains 5 main sections:Basic API Usage: All the fundamentals: signup for an account, get your API key, set environment variables on Windows / Linux / Mac, using the API in Python, setup billing, understand the pricing model, and OpenAI's usage policies. Of note is the chatbot tutorial, which goes over how to incorporate chat history into the model so that ChatGPT "remembers" what it said to you previously. A customer service chatbot will serve as a running example throughout this course.Prompt Engineering: ChatGPT Prompt Engineering for Developers - All about how to make ChatGPT do what you want it to do. We'll explore various example use-cases, such as getting ChatGPT to output structured data (JSON, tables), sentiment analysis, language translation, creative writing, text summarization, and question-answering. We'll explore techniques like chain-of-thought (CoT) prompting, and we'll even look at how to use ChatGPT to build a stock trading system!Retrieval Augmented Generation (RAG): Learn how to incorporate external data into LLMs. This powerful technique helps mitigate a common problem called "hallucination". It's critical if you have proprietary data (like product info for your company) that your LLM doesn't know about. You'll learn how semantic search / similarity search works, and how to implement it using FAISS (Facebook AI Similarity Search library). Learn how this will allow you to "chat with your data".Fine-Tuning:</stron
Welcome to our Machine Learning Projects course! This course is designed for individuals who want to gain hands-on experience in developing and implementing machine learning models. Throughout the course, you will learn the concepts and techniques necessary to build and evaluate machine-learning models using real-world datasets.We cover basics of machine learning, including supervised and unsupervised learning, and the types of problems that can be solved using these techniques. You will also learn about common machine learning algorithms, such as linear regression, k-nearest neighbors, and decision trees.ML Prerequisites LecturesPython Crash Course: It is an introductory level course that is designed to help learners quickly learn the basics of Python programming language.Numpy: It is a library in Python that provides support for large multi-dimensional arrays of homogeneous data types, and a large collection of high-level mathematical functions to operate on these arrays.Pandas: It is a library in Python that provides easy-to-use data structures and data analysis tools. It is built on top of Numpy and is widely used for data cleaning, transformation, and manipulation.Matplotlib: It is a plotting library in Python that provides a wide range of visualization tools and support for different types of plots. It is widely used for data exploration and visualization.Seaborn: It is a library built on top of Matplotlib that provides higher-level APIs for easier and more attractive plotting. It is widely used for statistical data visualization.Plotly: It is an open-source library in Python that provides interactive and web-based visualizations. It supports a wide range of plots and is widely used for creating interactive dashboards and data visualization for the web.
Master Machine Learning & AI Engineering — From Data Analytics to Agentic AI SolutionsLaunch your career in AI with a comprehensive, hands-on course that takes you from beginner to advanced. Learn Python, data science, classical machine learning, and the latest in AI engineering—including generative AI, transformers, and LLM agents / agentic AI.Why This Course?Learn by DoingWith over 145 lectures and 21+ hours of video content, this course is built around practical Python projects and real-world use cases—not just theory.Built for the Real WorldLearn how companies like Google, Amazon, and OpenAI use AI to drive innovation. Our curriculum is based on skills in demand from leading tech employers.No Experience? No ProblemStart from scratch with beginner-friendly lessons in Python and statistics. By the end, you’ll be building intelligent systems with cutting-edge AI tools.A Structured Path from Beginner to AI Engineer1. Programming FoundationsStart with a crash course in Python, designed for beginners. You’ll learn the language fundamentals needed for data science and AI work.2. Data Science and StatisticsBuild a solid foundation in data analysis, visualization, descriptive and inferential statistics, and feature engineering—essential skills for working with real-world datasets.3. Classical Machine LearningExplore supervised and unsupervised learning, including linear regression, decision trees, SVMs, clustering, ensemble models, and reinforcement learning.4. Deep Learning with TensorFlow and KerasUnderstand neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), using real code examples and exercises.5. Advanced AI Engineering and Generative AIGo beyond traditional ML to learn the latest AI tools and techniques:Transform
Artificial Intelligence is transforming the world — and AI agents are at the heart of that revolution. From virtual assistants and research bots to automated problem-solvers, agents are the next leap beyond simple chatbots. In this hands-on course, you’ll learn step-by-step how to build your own working AI agent using LangChain, Python, and the OpenAI API — no advanced technical experience required.We’ll start from the very beginning. You’ll understand what AI really means, how agents differ from chatbots, and where they’re being used in the real world. Then, you’ll dive into the practical side — setting up your Python environment, installing key tools, and connecting APIs like OpenAI and SerpAPI to bring intelligence and search power to your creations.Once your setup is complete, we’ll explore the core concepts of LangChain — including chains, agents, prompts, and tools. You’ll learn how to create prompt templates, build your own tools, and finally, connect them all to create an AI agent that can think, search, and respond intelligently. By the end, you’ll not only understand how LangChain works, but also how to build, test, and customize your own AI-powered assistants for real-world use.Whether you’re an aspiring developer, a curious beginner, or an entrepreneur exploring the future of automation, this course gives you the practical foundation you need to start building with confidence.By completing this course, you’ll be able to: Understand the fundamentals of AI and LangChain Set up your development environment from scratch Build and test working AI agents using Python and OpenAI Extend your agents with real-world tools and APIsJoin today, and take your first step toward building intelligent AI agents that can reason, search, and act — all powered by LangChain.
Unlock the power of modern Natural Language Processing (NLP) and elevate your skills with this comprehensive course on NLP with a focus on Transformers. This course will guide you through the essentials of Transformer models, from understanding the attention mechanism to leveraging pre-trained models. If so, then this course is for you what you need! We have divided this course into Chapters. In each chapter, you will be learning a new concept for Natural Language Processing with Transformers. These are some of the topics that we will be covering in this course:Starting from an introduction to NLP and setting up your Python environment, you'll gain hands-on experience with text preprocessing methods, including tokenization, stemming, lemmatization, and handling special characters. You will learn how to represent text data effectively through Bag of Words, n-grams, and TF-IDF, and explore the groundbreaking Word2Vec model with practical coding exercises.Dive deep into the workings of transformers, including self-attention, multi-head attention, and the role of position encoding. Understand the architecture of transformer encoders and decoders and learn how to train and use these powerful models for real-world applications.The course features projects using state-of-the-art pre-trained models from Hugging Face, such as BERT for sentiment analysis and T5 for text translation. With guided coding exercises and step-by-step project walkthroughs, you’ll solidify your understanding and build your confidence in applying these models to complex NLP tasks.By the end of this course, you’ll be equipped with practical skills to tackle NLP challenges, build robust solutions, and a
Unlock the Power of Generative AI with ChatGPT – No Experience Needed!Welcome to Generative AI with ChatGPT for Beginners, your step-by-step guide to understanding and using one of today’s most powerful AI tools. Whether you're a student, professional, or simply AI-curious, this course will take you from basic concepts to hands-on applications with ease.We start with foundational topics—what is Artificial Intelligence, and how do Machine Learning, Deep Learning, and NLP (Natural Language Processing) relate? You'll gain a solid understanding of LLMs (Large Language Models), Transformers, tokenization, and key concepts like temperature, top-k, and top-p sampling.Next, we dive into how AI models are built, trained, and fine-tuned. Learn the difference between supervised, unsupervised, and reinforcement learning, and explore concepts like bias, overfitting, underfitting, and analytics types.Then it’s time to get practical. Through real-world examples, you'll learn how to use ChatGPT for writing, planning, data generation, image prompting, and even creative tasks. You'll explore prompt engineering, structure design, and how to verify vs. generate information effectively.We go further with advanced topics like Retrieval-Augmented Generation (RAG), embeddings, vector databases, and multimodal and diffusion models. You'll learn where and how ChatGPT gets its information—and how to guide it responsibly.Finally, we address the big questions: ethical use, hallucinations, job impact, privacy, and how to use AI responsibly with human oversight.Each section includes quizzes, use cases, and interactive hands-on demos to ensure your learning sticks.By the end of the course, you’ll not only understand how generative AI works—you’ll know how to use it confidently and ethically in everyday life and work.Let’s demystify AI together!
Welcome to “Build Powerful AI Agents: From LangChain to Local LLMs”, your all-in-one course to become a complete AI Agent Engineer. Whether you’re a developer, data scientist, or AI enthusiast, this course will guide you step-by-step through building intelligent, multimodal, and voice-based AI systems — from the cloud (OpenAI) to local environments (Ollama & MCP).By the end of this course, you’ll gain hands-on experience developing smart, interactive, and deployable AI agents that can think, talk, reason, and adapt — the same way top AI startups do it today.What You’ll LearnUnderstand how LangChain Agents work and how to integrate them with OpenAI APIs.Build Voice-based Emotion and Wellness Companions using Whisper & TTS.Create Virtual AI Talking Agents and Copilot Systems that perform autonomous tasks.Implement RAG-powered assistants using LLaMA 3.1 and Pinecone.Learn the basics of PyTorch for deep learning and model training.Explore Hugging Face and GPT models for NLP and dataset customization.Understand MCP (Model Context Protocol) and use FastMCP to connect LLMs with databases.Master Fine-Tuning techniques and understand the difference between Fine-Tuning vs RAG.Deploy Local LLMs (like Gemma and Qwen) using Google Colab + Ngrok for free hosting.Hands-on Labs and ImplementationsEnroll now and become the expert of Generative AI.
This hands-on course teaches you how to build professional level Generative AI Application, intelligent, autonomous AI Agents using MCP (Model Context Protocol) and modern LLM frameworks.Whether you’re an AI beginner or an experienced developer, this course will take you step-by-step through the tools, strategies, and architectures that power modern GenAI applications.What You’ll Learn:- Introduction to Generative AI and its role in modern development- Introduction to Large Language Models (LLMs) and how they power intelligent applications- Generative AI Architecture Basics – understand the core components of a Gen AI application- Advanced Gen AI Application Architecture for scalable and modular systems- How to apply the Retrieval-Augmented Generation (RAG) technique for enhanced responses- Choosing the Right Orchestration Framework for building LLM-powered apps- LangChain – A modern framework for LLM orchestration- LangChain Expression Language (LCEL) – Build AI flows with clean, declarative syntax- Deep dive into the LangChain Ecosystem for agents, tools, memory, and chains- Mastering Prompt Engineering – Learn to craft optimal prompts for LLMs- Level 1 Gen AI Applications – Basic AI-powered tools and assistants- LlamaIndex – An alternative to LangChain for RAG and LLM app orchestration- LLMOps (Large Language Model Operations) – Manage and monitor LLM Apps- Level 2 Gen AI Applications – Build intermediate systems with memory, tools, and retrieval- Develop Multimodal Gen AI Applications (text, image, audio integration)- Build and deploy AI Agents & Multi-Agent Systems using orchestration frameworks- Level 3 (Professional) Gen AI Applications – Real-time, scalable, production-ready systems- CI/CD for Gen AI – Deploy your Gen AI apps with automated pipelines- Understand and implement MCP (Model Context Protocol) - Ha
This 200+ day globally recognized, industry-focused bootcamp is your all-in-one training for mastering Artificial Intelligence, Data Science, Machine Learning (ML), Deep Learning (DL), and Generative AI (GenAI) from beginner to expert level with simplicity and depth. Designed for aspiring data scientists, software engineers, AI professionals, and innovation leaders, this course offers a blend of foundational theory, programming practice, machine learning applications, and real-world project. The curriculum aligns with current global AI trends and industry hiring standards.Whether you're targeting top roles in global tech firms, launching an AI-powered startup, or aiming to build a strong data science portfolio this bootcamp ensures you stay ahead in the global AI race.Core Modules (SEO Keywords: Data Science, Python, AI, Machine Learning, Generative AI)Data Science FundamentalsData Science Sessions Part 1 & 2 – Foundation of modern data science methodologies and approaches.Data Science vs Traditional Analysis – Comparing data science techniques with conventional statistical methods.Data Scientist Journey Parts 1 & 2 – Skills, roles, and global career pathways.Data Science Process Overview – End-to-end project lifecycle and workflows.Programming Essentials (Python & R for Data Science)Introduction to Python for Data Science – Syntax, structures, and data analysis workflows.Python Libraries: Numpy, Pandas, Matplotlib, Seaborn – Building blocks for data processing and visualization.Introduction to R – Fundamentals of R programming for statistics and machine learning.Data Structures and Functions – Hands-on practice in Python & R for real-world data operations.Data Collection & PreprocessingMethods of Data Collection – Surveys, A
If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass™ is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you’ll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We’re approaching a new era where only apps and games that are considered “smart” will survive. (Remember how Blockbuster went bankrupt when Netflix became a giant?) Jump the curve and adopt this innovative approach; the Complete iOS Machine Learning Masterclass™ will introduce Machine Learning in a way that’s both fun and engaging. In this course, you will: Master the 3 fundamental branches of applied Machine Learning: Image & Video Processing, Text Analysis, and Speech & Language RecognitionDevelop an intuitive sense for using Machine Learning in your iOS appsCreate 7 projects from scratch in practical code-along tutorialsFind pre-trained ML models and make them ready to use in your iOS appsCreate your own custom models Add Image Recognition capability to your apps Integrate Live Video Camera Stream Object Recognition to your apps Add Siri Voice speaking feature to your apps Dive deep into key frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit. Use Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder–even if you have zero experienceGet FREE unlimited hosting for one yearAnd more! This course is also full of practical use cases
Are you excited about the future of AI where intelligent agents can think, act, and collaborate to solve complex tasks autonomously? Welcome to the Complete Agentic AI Bootcamp with LangGraph and LangChain — your one-stop course to master the art of building agentic AI applications from scratch!This course is designed to teach you everything you need to know about Agentic AI, LangGraph, and LangChain — two of the most powerful frameworks for building intelligent AI agents and multi-agent systems.You will start by understanding the fundamentals of Agentic AI — how it differs from traditional AI models, the key components of agents (memory, tools, decision-making), and real-world use cases.We will then dive deep into LangGraph, a cutting-edge framework that helps you design complex agent workflows using graphs, events, and state transitions. You’ll also learn how to combine LangChain's power with LangGraph to build production-ready agent applications.Throughout the course, you will build real-world projects step-by-step, including:Creating single intelligent agents with memory and tool-usage capabilities.Designing multi-agent collaboration systems with message passing and shared goals.Implementing autonomous research assistants, task automation bots, and retrieval-augmented generation (RAG) agents.You will not just learn theory — you will build and deploy multiple end-to-end agentic applications, gaining real-world experience in constructing powerful AI systems.By the end of this course, you will have the skills and confidence to create your own AI agents and deploy complex agentic applications for various domains like search, research, task planning, customer support, and beyond.What You Will Learn:Core concepts behind Agentic AI and how intelligent agents operate.Hands-on mastery of LangGraph and LangChain for b
Unlock the full potential of AI technology with this comprehensive ChatGPT masterclass. This course takes you from the basics to advanced applications, teaching you how to leverage ChatGPT and other AI tools for business growth and professional development.Throughout this practical, hands-on course, you'll master the fundamentals of AI and ChatGPT while learning innovative strategies for implementing these powerful tools in your daily work. From understanding the core concepts of artificial intelligence to mastering advanced prompt engineering techniques, you'll gain the skills needed to stay ahead in today's AI-driven world.The course is structured to provide both theoretical knowledge and practical applications. You'll learn how to create high-quality content efficiently, automate repetitive tasks, and develop AI-powered solutions for your business challenges. We'll cover advanced topics such as prompt optimization, content creation workflows, and monetization strategies using AI tools.Whether you're an entrepreneur looking to scale your business, a content creator seeking to enhance your productivity, or a professional aiming to stay competitive in your industry, this course provides the tools and knowledge you need. Through real-world examples, hands-on exercises, and practical assignments, you'll develop the confidence to implement AI solutions effectively.Join us to transform your approach to work and business in the AI era. No technical background required - just bring your enthusiasm to learn and grow.
Welcome to "AI 4 Everyone: Build Generative AI & Computer Vision Apps"—a comprehensive course designed for anyone looking to unlock the power of AI, whether you are a non-technical professional, or an aspiring AI developer.In this course, you’ll learn how to automate tasks, create powerful applications, and interact with AI models without needing extensive coding knowledge. Even if you’re a beginner, this course will guide you through building practical AI tools that simplify your day-to-day work.What You Will Learn:Automating Tasks with AI: Learn how to write professional emails, summarize YouTube videos, create stunning images, and explain complex graphs—all without writing a single line of code.Developing AI-Powered Applications: Using Python and Streamlit, you’ll create real-world applications like:A Recipe Generator that creates recipes based on your requests.An AI Meal Planner that organizes your meals based on nutritional needs.A YouTube Video to Blog Converter that transforms videos into blog posts.A PDF Sorter to efficiently organize and categorize documents.Document & Database Interactions: Discover how to chat with and extract information from documents, including:Text-to-SQL LLM Applications that query SQL databases.Multi-language Invoice Extractor that extracts text from invoices in various languages.PDF Q&A and sorting: Interact with your PDF files and manage them without the need for training or fine-tuning Large Language Models.LangChain Agents for CSV & JSON: Learn advanced AI techniques, like using LangChain agents to interact with CSV and JSON files for Q&A purp
Du möchtest Machine Learning verstehen und dich zum Data Scientist ausbilden lassen? Dann ist dieser Kurs genau das Richtige für Dich!Komplettpaket Machine Learning: Alle Grundlagen in Python und Machine Learning Algorithmen mitsamt Evaluation und Feature Engineering. Dabei werden Modelle aus dem Supervised Learning und Clustering betrachtet, sowie das Deep Learning und der KI. Der Fokus liegt auf den aktuellen Themen Reinforcement Learning und Natural Language Processing.Hast du dich schonmal gefragt wie es wäre den aktuell relevantesten Skill zu lernen und...von KI Trends zu profitieren?Möglichkeit auf richtig gut bezahlte Jobs zu haben?mit Python komplexe Probleme spielerisch zu lösen?in der Welt der Künstlichen Intelligenz und Deep Learning mitzuwirken?All das ist möglich im Leben eines Data Scientist. Und mit diesem Kurs bekommst du die vollständige Ausbildung dazu.Abschnitt 1: IntroductionIm ersten Abschnitt des Kurses "Machine Learning Campus: Data Science mit Python" erhältst du eine Einführung in den Kurs. Die erste Lektion bietet einen Überblick über den gesamten Kurs, damit du die Struktur und die wichtigsten Themenbereiche kennenlernen kannst. In der zweiten Lektion stellt sich der Dozent vor und teilt seine Motivation sowie seine Ziele für den Kurs mit, um dir einen persönlichen Einblick zu geben.Abschnitt 2: VorarbeitIn diesem Abschnitt legst du das Fundament für die Arbeit mit Python und den notwendigen Tools. Zunächst lernst du, wie du Python und PyCharm einrichtest. Die darauf folgenden Lektionen vertiefen deine grundlegenden Kenntnisse in Python und führen dich schrittweise in die Welt der Datenwissenschaft ein. Der Abschnitt schließt mit der Einführung in wichtige Bibliotheken wie Numpy, Pandas, Mat
This course provides an in-depth exploration into LangChain, a framework pivotal for developing generative AI applications. Now fully updated for LangChain 1.0.x — including LCEL, LangGraph-based orchestration, the revamped Agents API, and the langchain_classic imports.Aimed at both beginners and experienced practitioners in the AI world, the course starts with the fundamentals, such as the basic usage of the OpenAI API, progressively delving into the more intricate aspects of LangChain.You'll learn about the intricacies of input and output mechanisms in LangChain and how to craft effective prompt templates for OpenAI models. The course takes you through the critical components of LangChain, such as Chains, Callbacks, and Memory, teaching you to create interactive and context-aware AI systems.Midway, the focus shifts to advanced concepts like Retrieval Augmented Generation (RAG) and the creation of Autonomous Agents, enriching your understanding of intelligent system design. Topics like Hybrid Search, Indexing API, and LangSmith will be covered, highlighting their roles in enhancing the efficiency and functionality of AI applications.Toward the end, the course integrates theory with practical skills, introducing Microservice Architecture in large language model (LLM) applications and the LangChain Expression Language. This ensures not only a theoretical understanding of the concepts but also their practical applications.This course is tailored for individuals with a foundational knowledge of Python, aiming to build or enhance their expertise in AI. The structured curriculum ensures a comprehensive grasp of LangChain, from basic concepts to complex applications, preparing you for the future of generative AI.
Are you looking for a Machine Learning and Deep Learning course explained in Tamil?This course is designed for Tamil-speaking learners who want to master AI, ML, and DL concepts from the basics to advanced with clear explanations and practical examples.Machine Learning and Deep Learning are at the core of Artificial Intelligence (AI) and are widely used in real-world applications such as speech recognition, computer vision, chatbots, healthcare, recommendation systems, and much more.In this A to Z Tamil course, we’ll cover everything step by step in simple Tamil explanations so that even beginners can understand complex concepts easily.What You’ll Learn in This CourseIntroduction to Machine Learning (ML) & Artificial Intelligence (AI)Types of Machine Learning:Supervised LearningUnsupervised LearningReinforcement LearningML Algorithms explained in Tamil:Linear & Logistic RegressionDecision Trees & Random ForestsKNN & Naive BayesClustering (K-Means, Hierarchical)Deep Learning ConceptsArtificial Neural Networks (ANN)Convolutional Neural Networks (CNN)Recurrent Neural Networks (RNN, LSTM, GRU)Transfer Learning & Pretrained ModelsWhy Take This Course?Explained 100% in Tamil – No confusion, easy to followCovers both theory and practical insightsA to Z coverage of Machine Learning and Deep LearningBeginner-friendly with real-world exampl
A warm welcome to the Deep Learning with TensorFlow course by Uplatz.TensorFlow is an end-to-end open-source machine learning / deep learning platform. It has a comprehensive ecosystem of libraries, tools, and community resources that lets AI/ML engineers, scientists, analysts build and deploy ML-powered deep learning applications. The name TensorFlow is derived from the operations which neural networks perform on multidimensional data arrays or tensors. Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain.TensorFlow is a machine learning framework that Google created and used to design, build, and train deep learning models. You can use the TensorFlow library do to numerical computations, which in itself doesn’t seem all too special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or tensors, that are communicated between these edges.In simple words, TensorFlow is an open-source and most popular deep learning library for research and production. TensorFlow in Python is a symbolic math library that uses dataflow and differentiable programming to perform various tasks focused on training and inference of deep neural networks. TensorFlow manages to combine a comprehensive and flexible set of technical features with great ease of use.There have been some remarkable developments lately in the world of artificial intelligence, from much publicized progress with self-driving cars to machines now composing imitations or being really good at video games. Central to these advances are a number of tools around to help derive deep learning and other machine learning models, with Torch, Caffe, and Theano amongst those at the fore. However, since Google Brain went open source in November 20
Course Description:Discover the fascinating world of Conversational AI with ChatGPT. This course is specifically designed for complete beginners who want to unlock the power of ChatGPT and create captivating conversations.Join us as we demystify the art of using ChatGPT effectively and teach you the secrets of writing compelling prompts. Learn the renowned 5R's of Prompt Engineering technique to craft queries that yield incredible results.In addition, we'll show you how to leverage ChatGPT to excel in job interviews. Imagine having a virtual assistant to help you prepare and rehearse for those critical moments of impressing potential employers.Understand the capabilities and limitations of ChatGPT, and gain the knowledge to navigate its boundaries. We'll guide you through setting up and using ChatGPT seamlessly on your Windows PC, Mac, iPhone, or Android device.Explore various use cases for ChatGPT, igniting your imagination and expanding your understanding of its potential. Plus, discover other exciting Generative AI tools that complement your learning journey.No prior experience is required. This course is tailored for individuals who are eager to learn and have fun experimenting with Conversational AI. All you need is a curious mind, a decent internet connection, and the willingness to embark on this transformative journey.Enroll in today and become a master of Conversational AI!
"Mastering Prompt Engineering: ChatGPT Tips & Best Practices" is a comprehensive Udemy course that focuses on the practical applications of prompt engineering. The course is designed to help you learn how to use ChatGPT effectively and optimize its output for your specific needs.The course starts with an introduction to prompt engineering, covering the basics of how it works and why it's important for working with large language models like ChatGPT. You'll then learn about the different types of prompts and prompt patterns, and how to design effective prompts that give you the results you want from ChatGPT.Throughout the course, you'll be presented with practical examples and best practices for prompt engineering. This way you'll learn how to create prompts that automate software development tasks, generate content for your blog, and solve complex problems in different fields.This course is constantly updated to ensure that you are getting the most relevant and up-to-date information on prompt engineering. As new techniques and best practices emerge, the course will be updated with new content to reflect these changes. You'll also have access to a community of learners and instructors, where you can ask questions, share your own work, and get feedback and support.By the end of the course, you'll have a deep understanding of prompt engineering and how to use ChatGPT (or any other Large Language Model) to achieve your goals more effectively and efficiently. Whether you're a software developer, content creator, or researcher, this course will help you unlock the full potential of large language models through prompt engineering.
This course contains the use of artificial intelligence :)COURSE WAS RE-RECORDED and supports- LangChain Version 1.0+**Ideal students are software developers / data scientists / AI/ML Engineers**Welcome to the AI Agents with LangChain and LangGraph Udemy course - Unleashing the Power of Agentic AI!This course is designed to teach you how to QUICKLY harness the power the LangChain & LangGraph libraries for LLM applications and Agentic AI. This course will equip you with the skills and knowledge necessary to develop cutting-edge LLM solutions for a diverse range of topics.Please note that this is not a course for beginners. This course assumes that you have a background in software engineering and are proficient in Python. I will be using Pycharm IDE but you can use any editor you'd like since we only use basic feature of the IDE like debugging and running scripts .What You’ll Build: No fluff. No toy examples. You’ll build:Search AgentDocumentation Helper – A chatbot over Python package docs (and any data you choose), using advanced retrieval and RAG.Slim ChatGPT Code Interpreter – A lightweight code execution assistant.Prompt Engineering Theory SectionIntroduction to LangGraph Introduction to Model Context Protocol (MCP)Ice Breaker Agent – An AI agent that searches Google, finds LinkedIn and Twitter profiles, scrapes public info, and generates personalized icebreakers.The topics covered in this course include:AI AgentsAgentic AIAI EngineeringLangChain, LangGraphLLM + GenAI History
चैटजीपीटी कोर्स बेगिनर्स के लिएक्या आप जानते हैं कि आप ChatGPT क्या है और इसका उपयोग क्यों करना चाहिए? क्या आप तैयार हैं इस उपकरण की माहिती और उपयोग सीखने के लिए? अगर हां, तो यह कोर्स आपके लिए है!कौन कौन से छात्र इस कोर्स के लिए हैं:तकनीकी ज्ञान के बिना भी, यह शुरुआती स्तर का कोर्स है, तो यह किसी भी प्रारंभिक सीखने वाले के लिए उपयुक्त है.व्यावासिक योजनाओं और प्रस्तावनाओं को तैयार करने वाले व्यक्तियों के लिए, जो उच्च गुणवत्ता वाले प्रतिपूर्ण आलेख और योजनाएँ बनाना चाहते हैं.कोडिंग और प्रोग्रामिंग के बिना भी, डेवलपर्स और टेक्निकल पेशेवर्स के लिए, जो टेक्स्ट और लिखित सामग्री को और भी सुधारना चाहते हैं.इस कोर्स से छात्र क्या सीखेंगे:ChatGPT के आविष्कार से जुड़े आवश्यक ज्ञान के साथ, वे यह सीखेंगे कि कैसे इसका उपयोग करते हैं और क्यों।उन्हें विभिन्न विषयों पर उच्च गुणवत्ता वाले लिखित आलेख और व्यावासिक योजनाएँ बनाने के लिए उपयुक्त तरीकों की समझ होगी.वे कैसे ChatGPT का उपयोग करके कोडिंग और प्रोग्रामिंग के काम में मदद कर सकते हैं.इस कोर्स से आपको क्या लाभ होगा:क्वालिटी और सुंदर लिखित सामग्री की तैयारी करने के लिए एक शक्तिशाली उपकरण का उपयोग करने की क्षमता.उच्च गुणवत्ता वाले व्यवसायिक योजनाओं और प्रस्तावनाओं का निर्माण करने की कौशल.ChatGPT को साहित्यिक और प्रोग्रामिंग जगत में एक सशक्त साथी के रूप में उपयोग करने की योग्यता.इस कोर्स के माध्यम से, आप आपके व्यवसाय को नई ऊँचाइयों तक पहुँचाने के लिए ChatGPT का उपयोग करने के सारे रहस्य जानेंगे, और विभिन्न क्षेत्रों में आपकी सामग्री की मानकता बढ़ाएंगे। अब इस दुनिया में अपने स्वप्नों को पूरा करने के लिए तैयार हों!Call to Action: अगर आप अपने लेखन और व्यवसाय को नई ऊँचाइयों तक पहुँचाना चाहते हैं, तो अब ही इस कोर्स में शामिल हों! सीखिए ChatGPT का उपय
You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in Python, right?You've found the right Neural Networks course!After completing this course you will be able to:Identify the business problem which can be solved using Neural network Models.Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.Create Neural network models in Python using Keras and Tensorflow libraries and analyze their results.Confidently practice, discuss and understand Deep Learning conceptsHow this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical.Why should you choose this course?This course covers all the steps that one should take to create a predictive model using Neural Networks.Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniqu
Who Is This Course For?Are you curious about Artificial Intelligence but overwhelmed by buzzwords like NLP, Machine Learning, Deep Learning, and Generative AI? Do you want to understand AI without getting lost in technical jargon? If so, this course is perfect for you! No prior experience required! Whether you're a complete beginner, a student exploring AI for the first time, a professional looking to future-proof your career, or simply someone fascinated by how AI is shaping the world—you’ll walk away with clear, practical knowledge that puts you ahead of the curve.Why This Course?AI is no longer the future—it’s the present! Companies worldwide are integrating AI into their businesses, and those who understand it have a massive advantage in any industry. This course will give you a strong foundation in AI concepts without requiring coding or advanced mathematics. You’ll not only understand what AI is but also how it works and where it's headed, making you confident in conversations, career choices, and future learning. What makes this course special?Clarity over complexity – AI explained in simple, beginner-friendly terms.Practical relevance – Understand AI's real-world applications and how it's shaping industries.Guided learning roadmap – Follow Andrew Ng’s structured approach to mastering AI after this course.Don’t miss out on the AI revolution! Start learning today and gain the knowledge that will set you apart in the world of tomorrow. Enroll now!
HERE IS WHY YOU SHOULD TAKE THIS COURSEThis course is complete guide to both Supervised and Unsupervised learning using Python.This means,this course covers all the main aspects of practical Data Science and if you take this course you can do away withtaking other course or buying books on python based Data science .In this age of Big data companies across the globe use python to sift through the Avalache of information at their disposal..By becoming proficient in unsupervised and supervised learning in python,you can give your company a competitive edge and boost your careeer to the next level.LEARN FROM AN EXPERT DATA SCIENCE WITH 3+ YEARS OF EXPERIENCE:My Name is Aakash Singh and I had also recently published my Research Paper in INTERNATIONAL JOURNAL IJSR on Machine Learning Dataset.This course will give you robust grounding in the main aspects of Machine Learning-Clustering and Classification.NO PRIOR PYTHON OR STATISTICS OR MACHINE LEARNING KNOWLEDGE IS REQUIRED:you will start by absorbing the most valuable python Data science basics and techniques.I use easy to understand hands on methods to simplify and address even the most difficult conceptsin python.My course will help you to implement the methods using real data obtained from different sources.After using this course you will easily use package like numpy,pandas,and mathplotlib to work with real data in python..We will go through lab section on jupyter notebook terminal .we will go through lots of real life examples for icreasing practical side knowledge of the programming and we should not neglect theory section als,which is essential for this course,by the end of this course you will be able to code in python language and feel confident with machine learning and you will also be able to create your own program amd implement were you want.Most
In this course I will teach you how to use langchain to build LLM powered Applications and I will be using Open source models from hugging face What is LangChain?LangChain serves as a framework aimed at streamlining the development of applications utilizing Large language models. Functioning as a language model integration framework, LangChain's applications align closely with those of language models, spanning document analysis, summarization, chatbots, and code analysis.What is an LLM?A Large Language Model (LLM) is a type of artificial intelligence model that is trained on a vast amount of text data. It’s designed to generate human-like text based on the input it receives.In this course, I will be using LLMs such as Llama 2 7B and Mistral 7B.What is LCEL?LangChain Expression Language (LCEL) emerges as a declarative method within the LangChain framework, enabling effortless composition of chains. From its inception, LCEL prioritizes seamless transition from prototypes to production, accommodating a spectrum of complexities, from straightforward "prompt + LLM" sequences to intricate chains comprising hundreds of steps. Noteworthy features encompass streaming support for optimal time-to-first-token, asynchronous capabilities for versatile API usage, and optimized parallel execution for reduced latency. LCEL further offers configurations for retries, fallbacks, and access to intermediate results, enhancing reliability and debugging.In this course you learn - Langchain Basics - Langchain Expression Language- Chains- Memory- Agents and Tools- RAG etcDisclaimer: In this course I won't be using Open Ai API instead I would be using Open source models from hugging face and i will be using windows, kaggle
Embark on a transformative journey into the world of Data Analytics, Data Science, and Machine Learning, where you’ll learn the essential skills, tools, and mindsets to become a successful data professional. This comprehensive program is designed to take you from beginner to advanced, equipping you with the knowledge and practical experience needed to excel in the field.Whether you’re looking to kickstart a career in data analytics or enhance your existing skills, this course will empower you to succeed in the dynamic world of data. Join us on this exciting path and unlock your potential in just 60–100 days of disciplined learning.Why This Course MattersMost learners struggle with fragmented resources, inconsistent guidance, or theory-heavy content that doesn’t build real competence. This course solves that problem. It’s structured to provide step-by-step, cumulative, and daily progress — helping you turn knowledge into capability, and capability into career readiness.We are in the AI revolution, and every industry is transforming with tools like ChatGPT, Stable Diffusion, and AI copilots for writing, coding, design, analytics, and more. This course ensures you don’t just learn theory — you’ll build real-world solutions that make you job-ready.1. Foundations of Data Analytics, Data Science & PythonLearn how to think like a data scientist, not just how to write code.Python fundamentals: variables, loops, conditionals, functions, data structures.Clean, modular, reusable coding practices for data workflows.Importing and handling real-world datasets with Pandas and NumPy.Data types, memory optimization, and performance tuning.A-Z data cleaning and manipulation techniques: sorting, filtering, pivot tables, and charts.2. Excel, SQL, Python & Power BI Profi
This course is uniquely designed to be suitable for both experienced developers seeking to make that jump to Machine learning or complete beginners who don't understand machine learning and Artificial Neural Network from the ground up.In this course, we introduce a comprehensive training of multilayer perceptron neural networks or MLP in MATLAB, in which, in addition to reviewing the theories related to MLP neural networks, the practical implementation of this type of network in MATLAB environment is also fully covered.MATLAB offers specialized toolboxes and functions for working with Machine Learning and Artificial Neural Networks which makes it a lot easier and faster for you to develop a NN. At the end of this course, you'll be able to create a Neural Network for applications such as classification, clustering, pattern recognition, function approximation, control, prediction, and optimization.
This course is designed to cover maximum concepts of machine learning a-z. Anyone can opt for this course. No prior understanding of machine learning is required.Bonus introductions include Natural Language Processing and Deep Learning.Below Topics are covered Chapter - Introduction to Machine Learning- Machine Learning?- Types of Machine LearningChapter - Setup Environment - Installing Anaconda, how to use Spyder and Jupiter Notebook- Installing LibrariesChapter - Creating Environment on cloud (AWS)- Creating EC2, connecting to EC2- Installing libraries, transferring files to EC2 instance, executing python scriptsChapter - Data Preprocessing- Null Values- Correlated Feature check- Data Molding- Imputing- Scaling- Label Encoder- On-Hot EncoderChapter - Supervised Learning: Regression- Simple Linear Regression- Minimizing Cost Function - Ordinary Least Square(OLS), Gradient Descent- Assumptions of Linear Regression, Dummy Variable- Multiple Linear Regression- Regression Model Performance - R-Square- Polynomial Linear RegressionChapter - Supervised Learning: Classification- Logistic Regression- K-Nearest Neighbours- Naive Bayes- Saving and Loading ML Models- Classification Model Performance - Confusion MatrixChapter: UnSupervised Learning: Clustering- Partitionaing Algorithm: K-Means Algorithm, Random Initialization Trap, Elbow Method- Hierarchical Clustering: Agglomerative, Dendogram- Density Based Clustering: DBSCAN- Measuring UnSupervised Clusters Performace - Silhouette IndexChapter: UnSupervised Learning: Association R
Embark on a comprehensive journey into the world of artificial intelligence with "Learn AI - The Ultimate A-Z Guide." This course is designed to empower you with the skills and knowledge to apply AI in various aspects of life, whether you're a beginner or have prior experience. Each section offers detailed insights and practical applications tailored to enhance your proficiency and creativity with AI.Section 1: AI for Birthdays & PresentsDiscover innovative ways to use AI for creating personalized birthday experiences and gifts. Learn to harness ChatGPT for unique gift ideas tailored to individual preferences, and delve into generating custom images using AI tools. Explore the practical side of bringing digital creations to life through Printify and understand the process of converting text descriptions into 3D models. This section also covers the intricacies of 3D printing, guiding you through each step to produce tangible models from your AI-generated designs. Additionally, learn about 3D scanning technologies, utilizing AI to capture and enhance real-world objects digitally.Section 2: AI for Health & FitnessTransform your health and fitness journey with AI-driven solutions that cater to your personal needs. This section explores using AI to craft tailored workout and meal plans, ensuring they align with your fitness goals and dietary preferences. Discover how AI can enhance your gym experience, from optimizing equipment usage to analyzing your form. Learn to create and import workout playlists using AI, making your fitness routine more engaging. Explore the applications of AI in grocery shopping, helping you make healthier choices, and delve into the realm of mental health with AI.Section 3: ChatGPT FeaturesUnlock the full potential of ChatGPT by mastering its diverse features. This section provides an in-depth exploration of custom GPTs, teaching you how to tailor them for s
In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error. Google famously announced that they are now "machine learning first", and companies like NVIDIA and Amazon have followed suit, and this is what's going to drive innovation in the coming years. Machine learning is embedded into all sorts of different products, and it's used in many industries, like finance, online advertising, medicine, and robotics. It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good. Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world? This course will go from basics to advance. Step by step approach will make its easy to understand Machine Learning. TIPS (for getting through the course): Watch it at 2x.Take handwritten notes. This will drastically increase your ability to retain the information.Write down the equations. If you don't, I guarantee it will just look like gibberish.Ask lots of questions on the discussion board. The more the better!Realize that most exercises will take you days or weeks to complete.Write code yourself, don't jus
This course provides a thorough introduction to the intersection of data science and machine learning, balancing theory, numerical methods (coding), and real-world applications. It is designed for students and beginners who want to build a strong foundation in the concepts, statistics, and mathematics that support modern data science and machine learning algorithms.No prior experience is required; this course starts with the fundamentals, making it an excellent choice for beginners ready to embark on their learning journey.The course covers essential topics, including:- The basics of data science- Data visualisation and storytelling- Linear and non-linear regression methods- Explore the world of classification techniques with powerful tools like decision trees, random forests, and neural networks to unlock insights from your data. - Dive into unsupervised learning, where you can discover hidden patterns and groupings in your data using innovative clustering methods like spectral clustering. By the end of this course, students will be able to:- Apply quantitative modelling and data analysis techniques to solve real-world problems.- Effectively communicate findings through data visualisation.- Demonstrate proficiency in statistical data analysis techniques used in applied engineering.- Utilise data science principles to tackle engineering challenges.- Employ modern programming languages and computational tools to analyse big data.- Understand key concepts and gain in-depth knowledge of classical machine learning algorithms.- Implement classic machine learning algorithms to create intelligent systems.
Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information . Data is a fundamental part of our everyday work, whether it be in the form of valuable insights about our customers, or information to guide product,policy or systems development. Big business, social media, finance and the public sector all rely on data scientists to analyse their data and draw out business-boosting insights.Python is a dynamic modern object -oriented programming language that is easy to learn and can be used to do a lot of things both big and small. Python is what is referred to as a high level language. That means it is a language that is closer to humans than computer.It is also known as a general purpose programming language due to it's flexibility. Python is used a lot in data science. Machine learning relates to many different ideas, programming languages, frameworks. Machine learning is difficult to define in just a sentence or two. But essentially, machine learning is giving a computer the ability to write its own rules or algorithms and learn about new things, on its own. In this course, we'll explore some basic machine learning concepts and load data to make predictions.We will also be using SQL to interact with data inside a PostgreSQL Database.What you'll learnUnderstand Data Science Life CycleUse Kaggle Data SetsPerform Probability SamplingExplore and use Tabular DataExplore Pandas DataFrameManipulate Pandas DataFramePerform Data CleaningPerform Data VisualizationVisualize Qualitative DataExplore Machine Learning FrameworksUnderstand Supervised Machine LearningUse machine learning to predict value of a houseUse Scikit-LearnLoad datasets</p
In this course, you'll be learning the fundamentals of deep neural networks and CNN in depth.This course offers an extensive exploration of deep neural networks with a focus on Convolutional Neural Networks (CNNs).The course begins by delving into the fundamental concepts to provide a strong foundation for learners.Initial sections of the course include:Understanding what deep learning is and its significance in modern machine learning.Exploring the intricacies of neural networks, the building blocks of deep learning.Discovering where CNNs fit into the larger landscape of machine learning techniques.In-depth examination of the fundamentals of Perceptron Networks.Comprehensive exploration of Multilayer Perceptrons (MLPs).A detailed look into the mathematics behind feed forward networks.Understanding the significance of activation functions in neural networks.A major portion of the course is dedicated to Convolutional Neural Networks (CNNs):Exploring the architecture of CNNs.Investigating their applications, especially in image processing and computer vision.Understanding convolutional layers that extract relevant features from input data.Delving into pooling layers, which reduce spatial dimensions while retaining essential information.Examining fully connected layers for making predictions and decisions.Learning about design choices and hyperparameters influencing CNN performance.The course also covers training and optimization of CNNs:Understanding loss functions and their role in training.Grasping the concept of backpropagation.Learning techniques to prevent overfitting.Introduction to optimization algorithms for fine-tuning C
Get instant access to a workbook on Data Science, follow along, and keep for referenceIntroduce yourself to our community of students in this course and tell us your goals with data scienceEncouragement and celebration of your progress every step of the way: 25% > 50% > 75% & 100%30 hours of clear and concise step-by-step instructions, lessons, and engagementThis data science course provides participants with the knowledge, skills, and experience associated with Data Science. Students will explore a range of data science tools, algorithms, Machine Learning, and statistical techniques, with the aim of discovering hidden insights and patterns from raw data in order to inform scientific business decision-making.What you will learn:Data Science and Its TypesTop 10 Jobs in Data ScienceTools of Data ScienceVariables and Data in PythonIntroduction to PythonProbability and StatisticsFunctions in PythonOperator in PythonDataFrame with ExcelDictionaries in PythonTuples and loopsConditional Statement in PythonSequences in PythonIterations in PythonMultiple Regression in PythonLinear RegressionLibraries in PythonNumpy and SK LearnPandas in PythonK-Means ClusteringClustering of DataData Visualization with MatplotlibData Preprocessing in PythonMathematics in PythonData Visualization with PlotlyWhat is Deep Learning?Deep LearningNeural NetworkTensor FlowPostgreSQLMachine Learning and Data Science<
“This course contains the use of artificial intelligence.”Você tem curiosidade sobre Inteligência Artificial (IA), mas se sente perdido com tantos termos técnicos? Este curso para iniciantes é uma introdução clara e passo a passo à IA e aos Large Language Models (LLMs) . Em apenas uma hora , você aprenderá a história, a evolução e o uso prático das ferramentas de IA mais poderosas da atualidade, como ChatGPT, Gemini, Claude, além de modelos de código aberto como Llama, Mistral, DeepSeek e DBRX .Começamos com as origens da IA , desde ELIZA e os primeiros chatbots até redes neurais e Deep Learning. Em seguida, explicamos a revolução dos Transformers e o famoso artigo "Attention Is All You Need" , que transformou a IA para sempre. Você também descobrirá como os LLMs funcionam — tokens, treinamento, ajuste fino, janelas de contexto e RAG — explicados em uma linguagem simples e amigável para iniciantes.Em seguida, você explorará o cenário atual da IA , comparando GPT-4o, Gemini, Claude e os principais modelos de código aberto. Você aprenderá uma estrutura rápida para escolher o modelo certo para escrita, pesquisa, codificação, criatividade ou tarefas empresariais.Por fim, abordamos habilidades práticas essenciais :Noções básicas de engenharia de prompts – como escrever prompts melhores para obter resultados mais consistentes.Segurança, ética e uso responsável da IA – evite erros, proteja a privacidade e crie confiança nos resultados da IA.Ao final deste curso, você: - Explicará os fundamentos de IA e LLM com confiança. - Comparará os pontos fortes e fracos dos principais modelos. - Escreverá melhores prompts usando técnicas comprovadas. - Aplicará IA de fo
Practice questions to prepare for Generative AI LLMs Associate (NCA-GENL)!This certification is designed to validate foundational knowledge and practical skills in working with large language models (LLMs) and generative AI. This certification is ideal for professionals aiming to develop expertise in deploying and managing LLM-based solutions. Key focus areas include understanding transformer-based architectures, prompt engineering techniques for guiding model responses, and leveraging modern pretrained models to solve a range of natural language processing (NLP) tasks, such as text generation, token classification, and sentiment analysis. The certification covers best practices for working with human-labeled data and strategies for optimizing models for specific applications. This certification is ideal for those looking to strengthen their understanding of generative AI and advanced technologies within the rapidly evolving AI landscape.About the coursePrepare yourself for success in the Generative AI LLMs certification with this comprehensive mock exam course. This course is specifically designed to help you master the key concepts and skills needed to excel in the rapidly growing field of Generative AI, focusing on Large Language Models (LLMs).This course features six carefully crafted mock exams that closely mirror the format, difficulty, and scope of the actual certification exam. Each mock exam contains a diverse set of questions that test your knowledge on various topics, including the fundamentals of Generative AI, architecture and deployment of LLMs, model training and fine-tuning, ethical considerations, and specific tools and platforms for AI development.What sets this course apart is the detailed explanations provided for each question. After completing each exam, you will not only see which answers you got right or wrong but also receive in-depth explanations that clarify why certain answers are correct. This approach
A warm welcome to the Data Science, Artificial Intelligence, and Machine Learning with Python course by Uplatz.Python is a high-level, interpreted programming language that is widely used for various applications, ranging from web development to data analysis, artificial intelligence, automation, and more. It was created by Guido van Rossum and first released in 1991. Python emphasizes readability and simplicity, making it an excellent choice for both beginners and experienced developers.Data ScienceData Science is an interdisciplinary field focused on extracting knowledge and insights from structured and unstructured data. It involves various techniques from statistics, computer science, and information theory to analyze and interpret complex data.Key Components:Data Collection: Gathering data from various sources.Data Cleaning: Preparing data for analysis by handling missing values, outliers, etc.Data Exploration: Analyzing data to understand its structure and characteristics.Data Analysis: Applying statistical and machine learning techniques to extract insights.Data Visualization: Presenting data in a visual context to make the analysis results understandable.Python in Data SciencePython is widely used in Data Science because of its simplicity and the availability of powerful libraries:Pandas: For data manipulation and analysis.NumPy: For numerical computations.Matplotlib and Seaborn: For data visualization.SciPy: For advanced statistical operations.Jupyter Notebooks: For interactive data analysis and sharing code and re
This course is diving into Generative AI State-Of-Art Scientific Challenges. It helps to uncover ongoing problems and develop or customize your Own Large Models Applications. Course mainly is suitable for any candidates(students, engineers,experts) that have great motivation to Large Language Models with Todays-Ongoing Challenges as well as their deeployment with Python Based and Javascript Web Applications, as well as with C/C++ Programming Languages. Candidates will have deep knowledge on TensorFlow , Pytorch, Keras models, HuggingFace with Docker Service. In addition, one will be able to optimize and quantize TensorRT frameworks for deployment in variety of sectors. Moreover, They will learn deployment of LLM quantized model to Web Pages developed with React, Javascript and FLASKHere you will also learn how to integrate Reinforcement Learning(PPO) to Large Language Model, in order to fine them with Human Feedback based. Candidates will learn how to code and debug in C/C++ Programming languages at least in intermediate level.LLM Models used: The Falcon, LLAMA2, BLOOM, MPT, Vicuna,FLAN-T5, GPT2/GPT3, GPT NEOXBERT 101, Distil BERTFINE-Tuning Small Models under supervision of BIG ModelsImage Generation :LLAMA modelsGemini Dall-E OpenAIHugging face ModelsLearning and Installation of Docker from scratchKnowledge of Javscript, HTML ,CSS, BootstrapReact Hook, DOM and Javacscript Web DevelopmentDeep Dive on
TensorFlow is quickly becoming the technology of choice for deep learning, because of how easy TF makes it to build powerful and sophisticated neural networks. The Google Cloud Platform is a great place to run TF models at scale, and perform distributed training and prediction. This is a comprehensive, from-the-basics course on TensorFlow and building neural networks. It assumes no prior knowledge of Tensorflow, all you need to know is basic Python programming. What's covered: Deep learning basics: What a neuron is; how neural networks connect neurons to 'learn' complex functions; how TF makes it easy to build neural network modelsUsing Deep Learning for the famous ML problems: regression, classification, clustering and autoencodingCNNs - Convolutional Neural Networks: Kernel functions, feature maps, CNNs v DNNs RNNs - Recurrent Neural Networks: LSTMs, Back-propagation through time and dealing with vanishing/exploding gradientsUnsupervised learning techniques - Autoencoding, K-means clustering, PCA as autoencoding Working with imagesWorking with documents and word embeddingsGoogle Cloud ML Engine: Distributed training and prediction of TF models on the cloudWorking with TensorFlow estimators
Data Scientist is amongst the trendiest jobs, Glassdoor ranked it as the #1 Best Job in America in 2018 for the third year in a row, and it still holds its #1 Best Job position. Python is now the top programming language used in Data Science, with Python and R at 2nd place. Data Science is a field where data is analyzed with an aim to generate meaningful information. Today, successful data professionals understand that they require much-advanced skills for analyzing large amounts of data. Rather than relying on traditional techniques for data analysis, data mining and programming skills, as well as various tools and algorithms, are used. While there are many languages that can perform this job, Python has become the most preferred among Data Scientists.Today, the popularity of Python for Data Science is at its peak. Researchers and developers are using it for all sorts of functionality, from cleaning data and Training models to developing advanced AI and Machine Learning software. As per Statista, Python is LinkedIn's most wanted Data Science skill in the United States.Data Science with R, Python and Spark Training lets you gain expertise in Machine Learning Algorithms like K-MeansClustering, Decision Trees, Random Forest, and Naive Bayes using R, Python and Spark. Data Science Trainingencompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introductionto Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases onMedia, Healthcare, Social Media, Aviation and HR.CurriculumIntroduction to Data ScienceLearning Objectives - Get an introduction to Data Science in this module and see how Data Sciencehelps to analyze large and unstructured data with different tools.Topics:What is Data Science? What does Data Science involve?Era of Data Science Business Intelligence vs Data ScienceLife cycle
Become an AI Whisperer: Break into the field of prompt engineering, the most exciting and hottest new job in tech. Learn how to make Artificial Intelligence systems like ChatGPT and GPT-4 do exactly what you want, even if they've been programmed to do otherwise. Master their biases, take advantage of their design flaws, and become an expert prompter!Did you know a sentence as simple as "Ignore previous directions" can often confuse AIs as advanced as ChatGPT and grant you access to restricted functionality? This is exactly what prompt engineers do on a daily basis: they discover models' biases and exploit them to their advantage. Intrigued? Dive into the world of prompt engineering with our comprehensive video course, designed to unlock the potential of AI language models for a wide range of applications. Learn the principles, techniques, and advanced strategies for crafting effective prompts, hacking prompts, image prompts, and more. With a strong focus on practical examples, this course will equip you with the skills to transform AI language models into powerful tools for content creation, chatbots, coding assistants, and beyond. Embark on this journey to master prompt engineering and harness the true power of generative AI!What will you learn?Gain a deep understanding of the fundamentals, principles, and techniques of prompt engineering and its applications in various domains.Master the art of crafting effective prompts, utilizing tags, and employing advanced strategies to maximize the potential of AI language models.Develop the skills to create prompts for diverse applications, such as content creation, coding assistance, chatbot therapy, and more, using ChatGPT.Acquire the knowledge to secure prompt engineering efforts by understanding prompt hacking concepts and exploring advanced topics like AI-generated text detection and addressing biases.Learn image prompting techniques, incl
Interested in using Machine Learning in JavaScript applications and websites? Then this course is for you!This is the tutorial you've been looking for to become a modern JavaScript machine learning master in 2024. It doesn’t just cover the basics, by the end of the course you will have advanced machine learning knowledge you can use on you resume. From absolute zero knowledge to master - join the TensorFlow.js revolution.This course has been designed by a specialist team of software developers who are passionate about using JavaScript with Machine Learning. We will guide you through complex topics in a practical way, and reinforce learning with in-depth labs and quizzes.Throughout the course we use house price data to ask ever more complicated questions; “can you predict the value of this house?”, “can you tell me if this house has a waterfront?”, “can you classify it as having 1, 2 or 3+ bedrooms?”. Each example builds on the one before it, to reinforce learning in easy and steady steps.Machine Learning in TensorFlow.js provides you with all the benefits of TensorFlow, but without the need for Python. This is demonstrated using web based examples, stunning visualisations and custom website components.This course is fun and engaging, with Machine Learning learning outcomes provided in bitesize topics:Part 1 - Introduction to TensorFlow.jsPart 2 - Installing and running TensorFlow.jsPart 3 - TensorFlow.js Core ConceptsPart 4 - Data Preparation with TensorFlow.jsPart 5 - Defining a modelPart 6 - Training and Testing in TensorFlow.jsPart 7 - TensorFlow.js PredictionPart 8 - Binary ClassificationPart 9 - Multi-class ClassificationPart 10 - Conclusion & Next StepsAs a bonus, for every student, we provide you wit
*** NOW IN TENSORFLOW 2 and PYTHON 3 ***Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.Learn about one of the most powerful Deep Learning architectures yet!The Recurrent Neural Network (RNN) has been used to obtain state-of-the-art results in sequence modeling.This includes time series analysis, forecasting and natural language processing (NLP).Learn about why RNNs beat old-school machine learning algorithms like Hidden Markov Models.This course will teach you:The basics of machine learning and neurons (just a review to get you warmed up!)Neural networks for classification and regression (just a review to get you warmed up!)How to model sequence dataHow to model time series dataHow to model text data for NLP (including preprocessing steps for text)How to build an RNN using Tensorflow 2How to use a GRU and LSTM in Tensorflow 2How to do time series forecasting with Tensorflow 2How to predict stock prices and stock returns with LSTMs in Tensorflow 2 (hint: it's not what you think!)How to use Embeddings in Tensorflow 2 for NLPHow to build a Text Classification RNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition)All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflo
ChatGPT revolutionises businesses, how we work and greatly influences our lives. It is much more than a famous Web and mobile applications everyone is using now. Its creators recently released a publicly available API enabling creation of sophisticated AI Apps utilising the power of GPT models to most difficult Natural Language Processing tasks and beyond.This course aims at Python developers to teach how to harness the power of latest and greatest Large Language Models in custom, innovative applications, how to interface existing data in various formats with ChatGPT available through the API.You will learn the magic of LangChain - the Python Library delivering ever growing ecosystem of tools and integrations necessary to build the AI Apps. LangChain offers not only convenient wrappers around ChatGPT model APIs, but has plenty of ready-made classes and functions facilitating creation and use of Chat Memory, Vector DBs for semantic search of relevant documents, and blueprints of powerful Agents, capable of using Python functions in your environment to get access to local, proprietary data.The course is very practical and consists of dozens of practical demonstrations of Python code solving various AI tasks. You will get detailed, precise and in-depth explanation of all presented concepts and algorithms.All of the code used in the course is available for your download from GitHub repository. You can use it as a basis to further exploration and experimentation leading to quick and easy development of real-life AI Apps.
In this course, you will get to understand the foundational concepts that underlie the supervised machine-learning process. You will get to understand complex topics such as:Exploratory Data Analysis, Data Transformation and Feature Scaling, Evaluation Metrics, Algorithms, trainers, and models,Underfitting and Overfitting, Cross-validation, Regularization, and much moreYou will see these concepts come alive by doing a practical machine-learning exercise, rather than by looking at presentations. We will be using a non-cloud-based machine-learning tool called Model Builder, inside of Visual Studio. There will be zero coding involved (except for the very last lesson). But even though there is little coding involved, you will still get a very detailed understanding of complex machine-learning concepts.This course requires you to have at least some theoretical exposure to the concepts of supervised and unsupervised machine learning. This course is designed to build on a basic, theoretical understanding of machine learning by doing a practical machine-learning exercise. The concepts taught in this course are foundational and will be relevant in the future, regardless of what machine learning platform or programming language you use. In the process, you will also get some exposure to Visual Studio, code projects, solutions, and the Microsoft Machine Learning ecosystem. But that is just a side benefit. This course focuses on machine learning itself, not the tools that are used.If you've already done any kind of machine learning or trained a model, this course might be too basic for you. This course may contain foundational knowledge that you may not have been taught before, but please be aware that this course is geared toward beginner and intermediate-level AI enthusiasts.
In this course, you will dive deep into the world of Generative AI with LLMs (Large Language Models), exploring the potential of combining LangChain with Python. You will implement proprietary solutions (like ChatGPT) and modern open-source models like Llama and Phi. Through practical, real-world projects, you'll develop innovative applications, including a custom virtual assistant and a chatbot that interacts with documents and videos. We'll explore advanced techniques such as RAG and agents, and use tools like Streamlit to create intuitive interfaces. You'll learn how to use these technologies for free in Google Colab and also how to run projects locally.In the introduction, you’ll be introduced to the theory of Large Language Models (LLMs) and their fundamental concepts. Additionally, we’ll explore the Hugging Face ecosystem, which offers modern solutions for Natural Language Processing (NLP). You'll learn to implement LLMs using both the Hugging Face pipeline and the LangChain library, understanding the advantages of each approach.The second part is focused on mastering LangChain. You'll learn to access open-source models, like Meta's Llama and Microsoft’s Phi, as well as proprietary LLMs, like OpenAI's ChatGPT. We'll explain model quantization to enhance performance and scalability. Key LangChain components, such as chains, templates, and tools, will be presented, along with how to use them to develop robust NLP solutions. Prompt engineering techniques will be covered to help you achieve more accurate results. The concept of RAG (Retrieval-Augmented Generation) will be explored, including information storage and retrieval processes. You’ll learn to implement vector stores and understand the importance of embeddings and how to use them effectively. We’ll also demonstrate how to use RAG to interact with PDF documents and web pages. Additionally, you'll have the opportunity to explore integrating agents and tools, like using LLMs to perform web searches and retrie
Interested in the field of Machine Learning? Then this course is for you! This course has been designed by experts so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative field of ML. This course is fun and exciting, but at the same time we dive deep into Machine Learning. we will be covering the following topics in a well crafted way: Tensors and TensorFlow on the Cloud - what neural networks, Machine learning and deep learning really are, how neurons work and how neural networks are trained. - Datalab, Linear regressions, placeholders, variables, image processing, MNIST, K- Nearest Neighbors, gradient descent, softmax and more Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. Course Overview Module 1- Introduction Gcloud Introduction Labs Module 2 - Hands on GCP Labs Module 2-Datalab Module 3-Machine Learning & Tensorflow Introduction to Machine Learning, Typical usage of Mechine Learning, Types, The Mechine Learning block diagram, Deep learning & Neural Networks, Labels, Understanding Tenser Flow, Computational Graphs, Tensors, Linear regression , Placeholders & variables, Image processing in Tensor Flow, Image as tensors, M-NIST – Introduction, K-nearest neighbors Algorithm, L1 distance, Steps in K- nearest neighbour implementation, Neural Networks i
Have you ever thought about how Large Language Models (LLMs) are transforming the world and creating unprecedented opportunities?"AI won't take your job, but someone who knows how to use AI might," says Richard Baldwin.Are you ready to master the intricacies of LLMs and leverage their full potential for various applications, from data analysis to the creation of chatbots and AI agents?Then this course is for you!Dive into 'LLM Mastery: ChatGPT, Gemini, Claude, Llama, OpenAI & APIs'—where you will explore the fundamental and advanced concepts of LLMs, their architectures, and practical applications. Transform your understanding and skills to lead in the AI revolution.This course is perfect for developers, data scientists, AI enthusiasts, and anyone who wants to be at the forefront of LLM technology. Whether you want to understand neural networks, fine-tune AI models, or develop AI-driven applications, this course offers everything you need.What to expect in this course:Comprehensive Knowledge of LLMs:Understanding LLMs: Learn about parameters, weights, inference, and neural networks.Neural Networks: Understand how neural networks function with tokens in LLMs.Transformer Architecture: Explore the Transformer architecture and Mixture of Experts.Fine-Tuning: Understand the fine-tuning process and the development of the Assistant model.Reinforcement Learning (RLHF): Dive into reinforcement learning with human feedback.Advanced Techniques and Future Trends:Scaling Laws: Learn about the scaling laws of LLMs, including GPU and data improvements.Future of LLMs: Discover the capabilities and future development
This course features 600+ Real and Most Asked Interview Questions for Machine Learning and Data Science that leading tech companies have asked. Are you ready to master machine learning and data science? This comprehensive course, Master Machine Learning and Data Science: 600+ Real Interview Questions is designed to equip you with the knowledge and confidence needed to excel in your data science career. With over 600 real interview questions and detailed explanations, you'll gain a deep understanding of core concepts, practical skills, and advanced techniques.What You’ll Learn:The essential maths behind machine learning, including algebra, calculus, statistics, and probability.Data collection, wrangling, and preprocessing techniques using powerful tools like Pandas and NumPy.Key machine learning algorithms such as regression, classification, decision trees, and model evaluation.Deep learning fundamentals, including neural networks, computer vision, and natural language processing.Whether you’re a beginner or a professional looking to sharpen your skills, this course offers practical knowledge, real-world examples, and interview preparation strategies to help you stand out in the competitive field of data science. Join us and take the next step toward mastering machine learning and data science!Sample Questions:Question 1:You are building a predictive model for customer churn using a dataset that is highly imbalanced, with a much larger number of non-churning customers than churning ones. What technique would you apply to improve model evaluation and ensure that the model is not biased by the imbalanced classes?A) Use k-fold cross-validation to assess model performance across all data splits. B) Use stratified sampling in your cross-validation to maintain the class distribution in each fold.
Do you want to become an AWS Machine Learning Engineer Using SageMaker in 30 days?Do you want to build super-powerful production-level Machine Learning (ML) applications in AWS but don’t know where to start?Are you an absolute beginner and want to break into AI, ML, and Cloud Computing and looking for a course that includes everything you need?Are you an aspiring entrepreneur who wants to maximize business revenues and reduce costs with ML but don’t know how to get there quickly and efficiently?Do you want to leverage ChatGPT as a programmer to automate your coding tasks?If the answer is yes to any of these questions, then this course is for you!Machine Learning is the future one of the top tech fields to be in right now! ML and AI will change our lives in the same way electricity did 100 years ago. ML is widely adopted in Finance, banking, healthcare, transportation, and technology. The field is exploding with opportunities and career prospectsAWS is the one of the most widely used cloud computing platforms in the world and several companies depend on AWS for their cloud computing purposes. AWS SageMaker is a fully managed service offered by AWS that allows data scientist and AI practitioners to train, test, and deploy AI/ML models quickly and efficiently.This course is unique and exceptional in many ways, it includes several practice opportunities, quizzes, and final capstone projects. In this course, students will learn how to create production-level ML models using AWS. The course is divided into 8 main sections as follows:Section 1 (Days 1 – 3): we will learn the following: (1) Start with an AWS and Machine Learning essentials “starter pack” that includes key AWS services such as Simple Storage Service (S3), Elastic Compute Cloud (EC2), Identity and Access Management (IAM) an
Hello there,Welcome to the " Machine Learning & Data Science with Python, Kaggle & Pandas " CourseMachine Learning A-Z course from zero with Python, Kaggle, Pandas and Numpy for data analysis with hands-on examplesMachine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning helps you stay ahead of new trends, technologies, and applications in this field.Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models. Python, machine learning, django, python programming, machine learning python, python for beginners, data science. Kaggle, statistics, r, python data science, deep learning, python programming, django, machine learning a-z, data scientist, python for data sciencePandas is an open source Python package that is most widely used for d
Welcome to "Machine Learning and Data Science with LangChain and LLMs"! This comprehensive course is designed to equip you with the skills and knowledge needed to harness the power of LangChain and Large Language Models (LLMs) for advanced data science and machine learning tasks.In today’s data-driven world, the ability to process, analyze, and extract insights from large volumes of data is crucial. Language models like GPT have transformed how we interact with and utilize data, allowing for more sophisticated natural language processing (NLP) and machine learning applications. LangChain is an innovative framework that enables you to build applications around these powerful LLMs. This course dives deep into the integration of LLMs within the data science workflow, offering hands-on experience with real-world projects.What You Will Learn?Throughout this course, you will gain a thorough understanding of how LangChain can be utilized in various data science applications, along with the practical knowledge of how to apply LLMs in different scenarios. Starting with the basics of machine learning and data science, we gradually explore the core concepts of LLMs and how LangChain can enhance data-driven solutions.Key Learning Areas:1. Introduction to Machine Learning and Data Science: Begin your journey by understanding the core principles of machine learning and data science, including the types of data, preprocessing techniques, and model-building strategies.2. Exploring Large Language Models (LLMs): Learn what LLMs are, how they function, and their applications in various domains. This section covers the latest advancements in language models, including their architecture and capabilities in text generation, classification, and more.3. LangChain Fundamentals: Discover the potential of LangChain as a tool for
Are you looking to master Data Science,Machine Learning (ML), Deep Learning(DL) and Natural Language Processing (NLP) from the ground up? This comprehensive course is designed to take you on a journey from understanding the basics to mastering advanced concepts, all while providing practical insights and hands-on experience.What You'll Learn:Foundational Concepts: Start with the basics of ML and NLP, including algorithms, models, and techniques used in these fields. Understand the core principles that drive machine learning and natural language processing.Advanced Topics: Dive deeper into advanced topics such as deep learning, reinforcement learning, and transformer models. Learn how to apply these concepts to build more complex and powerful models.Practical Applications: Gain practical experience by working on real-world projects and case studies. Apply your knowledge to solve problems in various domains, including healthcare, finance, and e-commerce.Mathematical Foundations: Develop a strong mathematical foundation by learning the math behind ML and NLP algorithms. Understand concepts such as linear algebra, calculus, and probability theory.Industry-standard Tools: Familiarize yourself with industry-standard tools and libraries used in ML and NLP, including TensorFlow, PyTorch, and scikit-learn. Learn how to use these tools to build and deploy models.Optimization Techniques: Learn how to optimize ML and NLP models for better performance and efficiency. Understand techniques such as hyperparameter tuning, model selection, and model evaluation.Who Is This Course For:This course is suitable for anyone interested in learning machine learning and natural language processing, from beginners to advanced learners. Whether you're a student, a professional look
This course is outdated because it is based on pytorch lightning and alot of thing has been changed since the release of this course. Further some of datasets in this course are no more available for public anymore. So I am not providing support for this course. I want to make this course free, but udemy is not allowing to do so because of content length. The reason why I am not archiving this course, because its still relevant if you want to gain concept of medical imaging competition. Greetings. This course is not intended for beginners, and it is more practically oriented. Though I tried my best to explain why I performed a particular step, I put little to no effort into explaining basic concepts such as Convolution neural networks, how the optimizer works, how ResNet, DenseNet model was created etc. This course is for those who have worked on CIFAR, MNIST data and want to work in real-life scenariosMy focus was mainly on how to participate in a competition, get data and train a model on that data, and make a submission. In this course PyTorch lightning is usedThe course covers the following topicsBinary ClassificationGet the dataRead dataApply augmentationHow data flows from folders to GPUTrain a modelGet accuracy metric and lossMulti-class classification (CXR-covid19 competition)Albumentations augmentationsWrite a custom data loaderUse publicly pre-trained model on XRayUse learning rate schedulerUse different callback functionsDo five fold cross-validations when images are in a folderTrain, save and load modelGet test predictions via ensemble learningSubmit predictions to the competition pageMulti-label classification (ODIR competition)
Ready for an electrifying plunge into the universe of language technology? Prepare to enter the thrilling realm of LangChain with "LangChain 101 for Beginners (OpenAI / ChatGPT / LLMOps)", where you'll be taught how to harness the power of LangChain and Large Language Models (LLMs) to build your very own Python applications.Our aim for this course is simple - to equip you with everything you need to embark on your LangChain adventure. You'll be walked through using different LLMs from industry giants OpenAI and HuggingFace, understand the magic of calling prompts, creating templates, and chaining these prompts together to create a robust, interactive system.But that's not all! We’ll dive into the heart of conversational chatbots and explore how memory works within LangChain. We'll wrap things up with a detailed tutorial on how you can apply these impressive LLMs to your own documents.This course isn’t just informative—it’s also seriously fun. Through the use of memes, real-world analogies, and an engaging, down-to-earth approach, we've designed this course to be an enjoyable journey into the world of LangChain.Say goodbye to those long, never-ending courses that are all fluff and no substance. This course is compact, to-the-point, and perfect for Python developers looking for a fast-track introduction to LangChain and LLMs. We know your time is precious, so we've packed all the essential information into one power-packed hour."LangChain 101 for Beginners" is your golden ticket to understanding and implementing LangChain. By the end of this course, you'll not only have a comprehensive understanding of LangChain, but also be ready to dive headfirst into your next project with a newfound arsenal of skills and knowledge.Don't wait—let's start scripting the future, together. Let’s dive into the incredible world of LangChain and Large Language Models, and have some fun along the way!
A comprehensive, free video course on YouTube that covers the essential Python libraries for data analysis and visualization, perfect for learning EDA.
Welcome to a game-changing learning experience with "ChatGPT for Deep Learning using Python Keras and TensorFlow". This unique course combines the power of ChatGPT with the technical depth of Python, Keras, and TensorFlow to offer you an innovative approach to tackling complex Deep Learning projects. Whether you're a beginner or a seasoned Data Scientist, this course will significantly enhance your skill set, making you more proficient and efficient in your work.Why This Course?Deep learning and Artificial Intelligence are revolutionizing industries across the globe, but mastering these technologies often requires a significant time investment (for theory and coding). This course cuts through the complexity, leveraging ChatGPT to simplify the learning curve and expedite your project execution. You'll learn how to harness the capabilities of AI to streamline tasks from data processing to complex model training, all without needing exhaustive prior knowledge of the underlying mathematics and Python code.Comprehensive Learning ObjectivesBy the end of this course, you will be able to apply the most promising ChatGPT prompting strategies and techniques in real-world scenarios:ChatGPT Integration: Utilize ChatGPT effectively to automate and enhance various stages of your Data Science projects, including coding, model development, and result analysis.Data Management: Master techniques for loading, cleaning, and visualizing data using Python libraries like Pandas, Matplotlib, and Seaborn.Deep Learning Modeling: Gain hands-on experience in constructing and fine-tuning Neural Networks for tasks such as Image Recognition with CNNs, Time Series prediction with RNNs and LSTMs, and classification and regression with Feedforward Neural Networks (FNN), using ChatGPT as your assistant.<strong
Welcome to Building Generative AI Projects with LLM, Langchain, GAN course. This is a comprehensive project based course where you will learn how to develop advanced AI applications using Large Language Models, integrate workflow using Langchain, and generate images using Generative Adversarial Networks. This course is a perfect combination between Python and artificial intelligence, making it an ideal opportunity to practice your programming skills while improving your technical knowledge in generative AI integration. In the introduction session, you will learn the basic fundamentals of large language models and generative adversarial networks, such as getting to know their use cases and understand how they work. Then, in the next section, you will find and download datasets from Kaggle, it is a platform that offers a diverse collection of datasets. Afterward, you will also explore Hugging Face, it is a place where you can access a wide range of ready to use pre-trained models for various AI applications. Once everything is ready, we will start building the AI projects. In the first section, we are going to build a legal document analyzer, where users can upload a PDF file, and AI will extract key information, summarize complex legal texts, and highlight important clauses for quick review. Next, we will develop an Excel data analyzer, enabling users to upload spreadsheets and leverage AI to identify trends, generate insights, and automate data analysis processes. Then after that, we will create an AI short story generator, where users can generate creative and engaging narratives based on simple prompts, making it a useful tool for writers and content creators. Following that, we will build an AI code generator, where users can input natural language descriptions, and AI will generate structured, functional code snippets, streamlining the coding process. In the next section, we will develop a Q&A customer support chatbot, capable of answering common inquiries b
Welcome to the Complete Data Science and Machine Learning Bootcamp, the only course you need to learn Python and get into data science.At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here's why:The course is taught by the lead instructor at the App Brewery, London's leading in-person programming bootcamp.In the course, you'll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix.This course doesn't cut any corners, there are beautiful animated explanation videos and real-world projects to build.The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback.To date, we’ve taught over 200,000 students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup.You'll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp.We'll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional.The course includes over 40+ hours of HD video tutorials and builds your programming knowledge while solving real-world problems.In the curriculum, we cover a large number of important data science and machine learning topics, such as:Data
Do not take this course if you are an ML beginner. This course is designed for those who are interested in pure coding and want to fine-tune LLMs instead of focusing on prompt engineering. Otherwise, you may find it difficult to understand.Welcome to "Mastering Transformer Models and LLM Fine Tuning", a comprehensive and practical course designed for all levels, from beginners to advanced practitioners in Natural Language Processing (NLP). This course delves deep into the world of Transformer models, fine-tuning techniques, and knowledge distillation, with a special focus on popular BERT variants like Phi2, LLAMA, T5, BERT, DistilBERT, MobileBERT, and TinyBERT.Course Overview:Section 1: IntroductionGet an overview of the course and understand the learning outcomes.Introduction to the resources and code files you will need throughout the course.Section 2: Understanding Transformers with Hugging FaceLearn the fundamentals of Hugging Face Transformers.Explore Hugging Face pipelines, checkpoints, models, and datasets.Gain insights into Hugging Face Spaces and Auto-Classes for seamless model management.Section 3: Core Concepts of Transformers and LLMsDelve into the architectures and key concepts behind Transformers.Understand the applications of Transformers in various NLP tasks.Introduction to transfer learning with Transformers.Section 4: BERT Architecture Deep DiveDetailed exploration of BERT's architecture and its importance in context understanding.Learn about Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) in BERT.Understand BERT fine-tuning and evaluation techniques.Section 5: Practical Fine-Tuning with BERT</strong
Welcome to the era of Artificial Intelligence, where everything is rapidly evolving. In this dynamic era, it's crucial to enhance your skills by acquiring the most essential, cutting-edge knowledge that is currently in high demand in the market: Artificial Intelligence. This course takes you on a comprehensive learning journey, delving into the most advanced concepts in AI, such asComputer VisionGenerative A.IRNNVariational AutoencoderPytorch With Python and C++Numpy and PandasAnd lot of more things..There are numerous cutting-edge concepts in high demand at the moment. I am formerly engaged in the Trustline security limited organization, where we harness real-world experience to create resilient AI solutions. I leverage this experience to instruct you on crafting advanced, industry-ready, robust A.I.In this course, we embark on a journey to develop AI across various domains, including stock market analysis, human face generation, image classification, and more. This course not only reinforces your programming and mathematical fundamentals but also equips you to build AI solutions in two distinct languages: Python and C++. This proficiency in both languages is a rare and valuable asset in the deep learning space.Furthermore, we explore best practices that enable the systematic creation of AI solutions. We delve into the theory of MLOPS (Machine Learning Operations), enhancing your capabilities and making your talents shine brightly in the competitive AI market.We also explore how Chat GPT LLM can enhance and expedite our AI development in the realm of Data Science. This section is particularly engaging, as Chat GPT serves as a valuable assistant in addressing repetitive and logic-free tasks, making our AI journey even more exciting and efficient.At the
Learn Deep Learning from scratch. It is the extension of a Machine Learning, this course is for beginner who wants to learn the fundamental of deep learning and artificial intelligence. The course includes video explanation with introductions (basics), detailed theory and graphical explanations. Some daily life projects have been solved by using Python programming. Downloadable files of ebooks and Python codes have been attached to all the sections. The lectures are appealing, fancy and fast. They take less time to walk you through the whole content. Each and every topic has been taught extensively in depth to cover all the possible areas to understand the concept in most possible easy way. It's highly recommended for the students who don’t know the fundamental of machine learning studying at college and university level.The main goal of publishing this course is to explain the deep learning and artificial intelligence in a very simple and easy way. All the codes have been conducted through colab which is an online editor. Python remains a popular choice among numerous companies and organization. Python has a reputation as a beginner-friendly language, replacing Java as the most widely used introductory language because it handles much of the complexity for the user, allowing beginners to focus on fully grasping programming concepts rather than minute details. Below is the list of different topics covered in Deep Learning:Introduction to Deep LearningArtificial Neural Network vs Biological Neural NetworkActivation FunctionsTypes of Activation functionsArtificial Neural Network (ANN) modelComplex ANN model Forward ANN modelBackward ANN modelPython project of ANN model<strong
Welcome to the SGLearn Series targeted at Singapore-based learners picking up new skillsets and competencies. This course is an adaptation of the same course by Jose Marcial Portilla and is specially produced in collaboration with Jose for Singaporean learners. If you are a Singaporean, you are eligible for the CITREP+ funding scheme, terms and conditions apply. --------------- Note from Jose .... Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:Programming with PythonNumPy with PythonUsing pandas Data Frames to solve complex tasksUse pandas to handle Excel FilesWeb scraping with pythonConnect Python to SQLUse matplotlib and seaborn for data visual
Dive into Computer Vision with PyTorch: Master Deep Learning, CNNs, and GPU Computing for Real-World Applications - 2024 Edition"Unlock the potential of Deep Learning in Computer Vision, where groundbreaking advancements shape the future of technology. Explore applications ranging from Facebook's image tagging and Google Photo's People Recognition to fraud detection and facial recognition. Delve into the core operations of Deep Learning Computer Vision, including convolution operations on images, as you master the art of extracting valuable information from digital images.In this comprehensive course, we focus on one of the most widely used Deep Learning frameworks – PyTorch. Recognized as the go-to tool for Deep Learning in both product prototypes and academia, PyTorch stands out for its Pythonic nature, ease of learning, higher developer productivity, dynamic approach for graph computation through AutoGrad, and GPU support for efficient computation.Why PyTorch?Pythonic: PyTorch aligns seamlessly with the Python programming language, offering a natural and intuitive experience for learners.Easy to Learn: The simplicity of PyTorch makes it accessible for beginners, allowing a smooth learning curve.Higher Developer Productivity: PyTorch's design prioritizes developer productivity, promoting efficiency in building and experimenting with models.Dynamic Approach for Graph Computation - AutoGrad: PyTorch's dynamic computational graph through AutoGrad enables flexible and efficient model development.GPU Support: PyTorch provides GPU support for accelerated computation, enhancing performance in handling large datasets and complex models.Course Highlights:Gain a foundational understanding of PyTorch, essential for delving into the world of Deep Learning.Learn GPU programming and explore how to access free GPU resources for efficient learning.Master the Auto
Master the End-to-End Machine Learning Process with Python, Mathematics, and Projects — No Prior Experience NeededThis course is not just another introductory tutorial. It is a complete and intensive roadmap, carefully crafted for beginners who want to become confident and capable Machine Learning practitioners. Whether you're a student, a job-seeker, or a working professional looking to transition into AI/ML, this course equips you with the core skills, hands-on experience, and deep understanding needed to thrive in today’s data-driven world.Why This Course Is DifferentThis masterclass solves both problems by following a clear, layered, and project-oriented curriculum that blends coding, theory, and practical intuition — so you not only know what to do, but why you're doing it.You’ll go step-by-step from foundational Python to building real ML models and deploying them in real-world workflows — even touching advanced topics like ensemble models, hyperparameter tuning, regularization, and generative AI.What You’ll Learn — Inside the Masterclass#______Foundations of Machine Learning and Artificial IntelligenceWhat is ML, how it differs from AI and Deep Learning.Key ML model types: Regression, Classification, Clustering.Understanding AI applications, Gen AI, and the future of intelligent systems.Knowledge checks to reinforce conceptual understanding.#______Python Programming from Scratch – for Absolute BeginnersStarting with variables, data types, conditionals, loops, and functions.Data structures: Lists, Sets, Tuples, Dictionaries with hands-on labs.Object-oriented programming, API requests, and web scraping with BeautifulSoup.Reading and writing real-world datasets using pandas.<
Part of the Applied Data Science with Python Specialization, this course provides an introduction to text mining and NLP. It covers understanding and manipulating text data in Python, including topic modeling and text classification.
This is the first course in the Machine Learning Specialization. It provides a broad introduction to modern machine learning, including supervised learning (linear regression, logistic regression, neural networks, and decision trees). You will build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
This Udacity course, developed by Google, provides a practical introduction to A/B testing. You will learn how to design and analyze A/B tests. The course covers topics such as metrics, sample size, and statistical significance.
Offered by Duke University, this beginner-level course covers the foundational math skills needed for data science.
This beginner-level course introduces the exciting field of Computer Vision and its applications in various industries. You will learn about computer vision, its applications, and how to process images using Python, Watson AI, and OpenCV. The course also covers building image classification models and custom classifiers.
This course provides a comprehensive overview of how machine learning functions in embedded systems. It teaches students how to train neural networks and deploy them to microcontrollers, a field also known as TinyML. The course is designed for beginners with no prior machine learning experience, but some familiarity with Arduino and microcontrollers is recommended.
For those who want to go beyond the basics, this course covers advanced deep learning topics using Keras. You'll learn about functional APIs, custom loss functions, and how to build more complex models.
This course provides a thorough introduction to handling missing data in R, with a focus on multiple imputation using the 'mice' package.
This beginner-level course explores how to use generative AI, specifically ChatGPT, to streamline the process of creating product documentation. It covers setting up AI tools, generating comprehensive product content, and refining the AI-generated documentation to meet specific needs.
This course provides an introduction to the foundations of data, strategy, and analytical tools like AI and Machine Learning as they apply to real estate. It includes lessons on machine learning, guest lectures from Columbia University professors, and a group project to design a real-world AI application.
A comprehensive, beginner-friendly course that covers AI's impact on design, from the basics and prompt engineering to integrating AI tools into your workflow, complete with portfolio-building exercises.
This course provides an introduction to AI agents, exploring their core concepts and functionalities. It is designed for those new to Make and takes an estimated 20-30 minutes to complete.
This instructor-led, live training is aimed at beginner-level to intermediate-level software developers who wish to integrate AI coding assistants into their development workflow.
This free online course is designed for both beginners and professionals, offering a blend of theoretical and practical insights into how AI can automate meal planning and optimize nutrient intake.
A free course designed for beginners that explores the applications of AI in nutrition, from generating personalized meal plans to analyzing food composition for optimal health.
Designed for business professionals without technical expertise, this 8-week course covers AI fundamentals, chatbots, automation, and Python for predictive analytics and financial modeling.
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