Beginner Deep Learning

Comprehensive deep learning specialization by Andrew Ng. Master neural networks, CNNs, RNNs, and modern deep learning architectures.

Comprehensive ML course covering regression, classification, clustering, deep learning, NLP, reinforcement learning.

Master TensorFlow 2 and Keras. ANNs, CNNs, RNNs, GANs, deployment.

Deep Learning A-Z: Hands-On Neural Networks

Natural Language Processing with Deep Learning

Chatbot Development with Python and Deep Learning

In this course, you'll be learning the fundamentals of deep neural networks and CNNs 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 CNNs 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 reference Introduce yourself to our community of students in this course and tell us your goals with data science Encouragement and celebration of your progress every step of the way: 25% &gt; 50% &gt; 75% &amp; 100%30 hours of clear and concise step-by-step instructions, lessons, and engagement This 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&nbsp; you will learn:Data Science and Its Types Top 10 Jobs in Data Science Tools of Data Science Variables and Data in Python Introduction to Python Probability and Statistics Functions in Python Operator in Python Data Frame with Excel Dictionaries in Python Tuples and loops Conditional Statement in Python Sequences in Python Iterations in Python Multiple Regression in Python Linear Regression Libraries in Python Numpy and SK Learn Pandas in PythonK-Means Clustering Clustering of Data Data Visualization with Matplotlib Data Preprocessing in Python Mathematics in Python Data Visualization with Plotly What is Deep Learning?Deep Learning Neural Network TensorFlow PostgreSQL Machine 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 (LL Ms) . 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, Deep Seek 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 LL Ms 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 LL Ms Associate (NCA-GENL)!This certification is designed to validate foundational knowledge and practical skills in working with large language models (LL Ms) 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 course Prepare yourself for success in the Generative AI LL Ms 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 (LL Ms).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 LL Ms, 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

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 models Using Deep Learning for the famous ML problems: regression, classification, clustering and autoencodingCN Ns - Convolutional Neural Networks: Kernel functions, feature maps, CNNs v DNNs RNNs - Recurrent Neural Networks: LSTMs, Back-propagation through time and dealing with vanishing/exploding gradients Unsupervised learning techniques - Autoencoding, K-means clustering, PCA as autoencoding Working with images Working with documents and word embeddings Google Cloud ML Engine: Distributed training and prediction of TF models on the cloud Working 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.&nbsp; 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 Linked In'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-Means Clustering, 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 on Media, Healthcare, Social Media, Aviation and HR.Curriculum Introduction to Data Science Learning 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 Science Life cycle

&nbsp;NOW&nbsp;IN&nbsp;TensorFlow 2 and PYTHON&nbsp;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 (RNNs) 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 data How to model time series data How to model text data for NLP (including preprocessing steps for text)How to build an RNNs&nbsp;using TensorFlow 2How to use a GRUs and LSTMs 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 NLP How to build a Text Classification RNNs 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&nbsp;Numpy,&nbsp;Matplotlib,&nbsp;and&nbsp;Tensorflo

Have you ever thought about how Large Language Models (LL Ms) 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 LL Ms 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 &amp; AP Is'—where you will explore the fundamental and advanced concepts of LL Ms, 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 LL Ms:Understanding LL Ms: Learn about parameters, weights, inference, and neural networks.Neural Networks: Understand how neural networks function with tokens in LL Ms.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 LL Ms, including GPU and data improvements.Future of LL Ms: Discover the capabilities and future development

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 &amp; 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&nbsp; the need for very little human intervention,Enable self-driving cars Helping quick response surveillance with smart CCTV systems, as the cameras now have an eye and a brain Creation of art&nbsp; with GANs, VAEs, and Diffusion Models Data 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 Hugging Face. We shall start by understanding how to build very simple mo

In this comprehensive course, you will master the fundamentals and advanced concepts of computer vision, focusing on Convolutional Neural Networks (CNNs) 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 Learn Throughout this course, you will gain expertise in:Introduction to Computer Vision Understanding 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 Vision Introduction 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 (CNNs)Introduction to CNNs architecture and its components.Understanding convolution layers, pooling layers, and fully connected layers.Implementing CNNs models using TensorFlow and PyTorch.Data Augmentation and Preprocessing Techniques for improving model performance through data augmentation.Using libraries like imgaug, Albumentations, and TensorFlow Data Pipeline.Transfer Learning for Computer Vision Utilizing pre-trained models such as Res Net, VGG, and Efficient Net.Fine-tuning and optimizing transfer learning models.Object Detection Models Exploring object detection algorithms like:YOLO (You Only Look Once)Faster R-CNNs Implement

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:&nbsp; * Le Net5,&nbsp; * Alex Net,&nbsp; * VGG-16,&nbsp; * Res Net,&nbsp; * Inception,&nbsp; * The lastest state of art Vision Transformer (ViT)- Many practical applications using famous datasets and sources such as:&nbsp; * Covid19 on X-Ray images,&nbsp; * CIFAR10,&nbsp; * Fashion MNIST,&nbsp; * BCCD,&nbsp; * COCO dataset,&nbsp; * 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

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?&nbsp;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 &amp; Frameworks: Master industry-standard tools like PyTorch and dive into CNNs, transfer learning with Res Net, 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:Num Py Crash Course Pandas Crash Course Neural Network Theory and Intuition How to Work with Torchvision datasets Convolutional Neural Network (CNNs)Long-Short Term Memory (LSTMs)and much more Since 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.

This course provides&nbsp;you to&nbsp;be able to build Deep Neural Networks models&nbsp;for different business domains&nbsp;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.&nbsp;We will also focus on the advanced topics in this lecture such as transfer learning, autoencoders, face recognition (including those models: VGG-Face, Google Face Net, Open Face and Facebook Deep Face).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.

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.&nbsp;S, you will finally get to know why Neural Network performs better &amp;&nbsp;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 &amp;&nbsp;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.

Hello there,Welcome to the " Complete Data Science &amp; Machine Learning A-Z with Python " Course Machine Learning &amp; Data Science all in one course with Python Data Visualization, Data Analysis Pandas &amp; Numpy, Kaggle Machine 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 i Phone’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 science Pandas is an open source Python package that is most widely used for

Hello there,Welcome to the “Machine Learning &amp; Data Science with Python &amp; Kaggle | A-Z” course.Data Science &amp; Machine Learning A-Z &amp; Kaggle with Heart Attack Prediction projects and Machine Learning Python projects Machine 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 i Phone’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 science Python instructors on OAK Academy specialize in everything from software d

Just launched with all modern best practices for building neural networks with TensorFlow and becoming a TensorFlow &amp;&nbsp;Deep&nbsp;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 Fundamentals Introduction to tensors (creating tensors)Getting information from tensors (tensor attributes)Manipulating tensors (tensor operations)Tensors and Num PyUsing @tf.function (a way to speed up your regular Python functions)Using GP Us with Tensor Flow1 — Neural Network Regression with TensorFlow Build TensorFlow sequential models with multiple layers Prepare data for use with a machine learning model

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 “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 &amp; 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 Future Data 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 demand Udemy 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

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

Welcome to this comprehensive hands-on course on YOL Ov10 for real-time object detection! YOL Ov10 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 YOL Ov10 for various object detection tasks.Throughout the course, you will learn how to set up and use YOL Ov10, label and create datasets, and train the model with custom data. The course is divided into three main parts:Part 1: Learning to Use YOL Ov10 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 YOL Ov10 models to detect objects in images. We will cover the following:Setting up the environment and installing necessary packages.Downloading pre-trained YOL Ov10 models.Performing object detection on sample images.Visualizing and interpreting detection results.Part 2: Labeling and Making a Dataset with Robo Flow In the second part, we will focus on creating and managing custom datasets using Robo Flow. This section will teach you how to:Create a project workspace on the Robo Flow 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 YOL Ov10.Part 3: Training with Custom Datasets The final section of the course is dedicated to training YOL Ov10 with your custom datasets. You will learn how to:Configure the training process, including setting parameters such as epochs and batch size.Train the YOL Ov10 model using your labeled dataset from Robo Flow.Monitor training progress and evaluate the trained model.

Hello there,Welcome to the “Python Programming: Machine Learning, Deep Learning | Python” course Python, machine learning, python programming, django, ethical hacking, data analysis, python for beginners, machine learning python, python bootcamp Python Machine Learning and Python Deep Learning with Data Analysis, Artificial Intelligence, OOP, and Python Projects Complete 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 i Phone’s Siri, are all technologies that function based on machine learning algorithms and mathe

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 Basics TensorFlow Basics Artificial Neural Networks Densely Connected Networks Convolutional Neural Networks Recurrent Neural Networks Auto Encoders Reinforcement Learning OpenAI 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 CP Us or GP Us 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

Interested in the field of Machine Learning? Then this course is for you!Designed &amp; Crafted by AI&nbsp;Solution&nbsp;Expert with 15 +&nbsp;years of relevant and hands on experience into Training ,&nbsp;Coaching and Development.Complete Hands-on&nbsp;AI Model Development with Python.&nbsp; Course Contents are:Understand Machine Learning in depth and in simple process. Fundamentals of Machine Learning Understand the Deep Learning Neural Nets with Practical Examples.Understand Image Recognition and Auto Encoders.Machine learning project Life Cycle Supervised &amp; Unsupervised Learning Data Pre-Processing Algorithm Selection Data Sampling and Cross Validation Feature Engineering Model Training and ValidationK -Nearest Neighbor AlgorithmK- Means Algorithm Accuracy Determination Visualization using Seaborn You will be trained to develop various algorithms for supervised &amp; unsupervised methods such as&nbsp; KNN , K-Means , Random&nbsp;Forest, XG Boost 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

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 Tzu You get the best information that I have compiled over years of trial and error and experience!Best wishes,Yılmaz ALACA

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 analysis Building and deploying Machine Learning models from scratch Exploring Data Science techniques, including data cleaning, visualization, and analysis Working with Big Data Analytics tools to handle massive datasets Implementing AI solutions for real-world projects and business applications Understanding key concepts in Deep Learning, Neural Networks, and Predictive Analytics Who This Course is For:Anyone passionate about leveraging AI and Big Data to make smarter decisions Why Choose This Course:Hands-on projects and real-world examples Learn from beginner-friendly to advanced concepts in a structured way Focused on practical applications that can boost your career or business Certificate after course complete By 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!

Growing Importance of Deep Learning Deep 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

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!

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 frameworks Practice-based learning through 134 exam-style questions across 4 modules Clarity on architectures such as CNNs, RNNs, LSTMs, and Transformers Hands-on readiness with TensorFlow and PyTorch fundamentals Awareness of exam strategies to manage time, avoid common pitfalls, and improve accuracy Who is this course for?Learners preparing for deep learning certification exams Professionals aiming to validate their AI/ML knowledge Students who want structured revision in neural networks and frameworks Important Note: This is not an official certification course and is not affiliated with any certifyin

Welcome to the gateway to your journey into Python for Machine Learning &amp; 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 &amp; Deep Learning? Enroll now and take the first step towards becoming a proficient AI practitioner!

This Course simplifies the advanced Deep Learning concepts like Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long Short Term Memory (LSTMs), Gated Recurrent Units(GRUs), 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.&nbsp; Case studies on topics like Object detection will also be included. TensorFlow and Keras basics and advanced concepts have been discussed in great detail.&nbsp; 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.&nbsp;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) OpenCV using Pythonk) Building and deploying Deep Neural Networks&nbsp;l) Professional Google TensorFlow developer&nbsp;m) Using Google Colab for writing Deep Learning coden) Python programming for Deep Neural Networks The Learners are advised to practice the TensorFlow code as they watch the videos on Programming from this course.&nbsp;First Few sections have been uploaded, The course is in updation phase and the remaining sections will be added soon.

Welcome to our Python &amp; 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!

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 YOL Ov8.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 YOL Ov8, Faster R-CNNs, and SSD.Explore image classification techniques, including CNNs architectures like Res Net, Inception, and Efficient Net.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 (MLPs) 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

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!

PyTorch&amp;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 Hugging Face yourself by colab. Each Section will have one assignment for you to think and code yourself. The Agenda is below.&nbsp; &nbsp;Agenda:Introduction Google Colaboratory Neuron Perceptron Make Your Perceptron Trainable Normalize Data Activation Function Loss Function Gradient Descent Elegant PyTorch Gradient Descent Final Project Final Project Explained Multi Layer Perceptron(MLPs)One Hot Encoding Prepare data for MLPs Define MLPs Train &amp; Evaluate MLPs Final Project for MLPFCNN ExplainedFCNN LOVE Letters Classification using MLPs Final Project For FCNNCNN ExplainedCNN Prepare data(Fashion MNIST) CNNs Define Model CNNs Train&amp;Evaluate ModelCNN Inference Final Project For CNNRNN ExplainedRNN Prepare dataRNN Define ModelRNN Train ModelRNN InferenceBERT Sesame StreetBERT Prepare Data IMDBBERT Model definitionBERT Model TrainingBERT Model Evaluation<p

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. &nbsp;What does a data scientist need to learn about Neural Networks? &nbsp;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

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 CNNs architecture you already know and love, to modern, novel architectures such as Res Net, 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 Mobile NetV2 which is both faster and more accurate than its predecessors.One best thing is you will understand the core basics of CNNs 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.

Master Real-Time Object Detection with Deep Learning Dive 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 YOL Ov3: Dive deep into real-time object detection and recognition.Training a YOL Ov3 Model for Real-Time License Plate Recognition: Learn to customize and train your own YOL Ov3 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 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 Kickstarter With 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 Py Charm, 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 everywhere You 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: ​​Java Script is one of the fundamental languages of the web. Java Script is easy to program in but some tasks are difficult. Java Script 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 Cand 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 Overview 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 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 RNNs. We will also highlight how autoencoders can be used for efficient data representation. Lastly, we will take you through some of th

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 topics Practice 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 programs Happy Coding,Vinay Phadnis :)

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

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&nbsp;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 AI At 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 certification Are there any pre-requisites?Students must have a basic knowledge of undergraduate level mathematics in areas like Linear Algebra, Probability, Statistics and Calculus.&nbsp; 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.

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

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 determination Ready 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 Linked In at Adam Cole Adam Cole BSc MBCS.

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 Development Basics of Computer Vision Basics of OpenCV js Computer Vision and Web Integration Graphical Interface Video Processing in the Browser using OpenCV.js Object Detection Custom Object Detection TensorFlow for Java Script Deep Learning on the Web Computer Vision Advanced Creating 10+ CV Web Apps Building a Photoshop Web Application with OpenCV.js Real-Time Face Detection in the Browser with OpenCV.js &amp; Haar Cascade Classifier Real-time Object Detection in the Browser using YOL Ov8 and TensorFlow.js Object Detection in Images &amp; Videos in the Browser using YOL Ov8 &amp; TensorFlow.js Personal Protective Equipment (PPE) Detection in the Browser using YOL Ov8 and TensorFlow.js American Sign Language (ASL) Letters Detection in the Browser using YOL Ov8 and TensorFlow.js Licence Plate Detection and Recognition in the Browser using YOL Ov8 and Tesseract.js

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 algorithms Machine 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 i Phone’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

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' fast.ai course), you’ll be pleasantly surprised at how much more you can understand.You'll be able to understand

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

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 Basics TensorFlow detailed,Keras,Sonnet etc Artificial Neural Networks Types of Neural network Feed forward network Radial basis network Kohonen Self organizing maps Recurrent neural Network Modular Neural networks Densely Connected Networks Convolutional Neural Networks Recurrent Neural Networks Machine 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

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,&nbsp; I started my career as analyst as gradually moved to data scientist hence&nbsp; I&nbsp;can understand what are programming prerequisites for data scientist. This course is created for absolute beginners of data science and machine learning.&nbsp; It covers all aspect of python languages required in data science machine learning and deep learning.

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&nbsp;Generativa y Agentes IA. ¡Únete a nosotros y desbloquea el potencial de la Inteligencia Artificial!

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.

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 competitions Check 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

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.

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 libraries Perform advanced image processing with Tensor FlowAP Is Understand 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 CNNs architectures with CNNs 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.&nbsp; 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.&nbsp; 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.&nbsp; 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.&nbsp; 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.&nbsp; Then, we tackle linear tr

Computer Vision With Deep Learningرؤية الكمبيوتر باستخدام التعلم العميقDescription This 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 (DNNs) - PyTorch . Convolutional Neural Network (CNNs)- TensorFlow.Semantic Segmentation.Object Detection.Instance Segmentation.Pose Estimation.Generative AI.Face Recognition.If you do not have prior experience in Machine Learning OR&nbsp;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 GANs 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

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.&nbsp;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 (Deep Fakes - 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