Beginner TensorFlow

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

This course provides a comprehensive, hands-on introduction to machine learning on the Google Cloud Platform, with a specific focus on Vertex AI. Students will learn about various GCP services, including compute, storage, and databases, before diving into machine learning workflows. The curriculum covers building and deploying models using GCP's AutoML for tabular, image, and text data, as well as custom model training and deployment on the AI Platform and Vertex AI. The course is designed to equip learners with the practical skills needed to create and manage machine learning pipelines on Google Cloud.

This course covers RNNs, LSTMs, and GRUs in TensorFlow. It includes projects on time series prediction, music generation, language translation, image captioning, spam detection, and action recognition.

Unlock the potential of Generative AI with our comprehensive course, "Mastering Generative AI: LL Ms, Prompt Engineering & More." This course is designed for both beginners and seasoned developers looking to deepen their understanding of the rapidly evolving field of artificial intelligence.In this course, you will explore a wide range of essential topics, including:· Python Programming: Learn the fundamentals of Python, the go-to language for AI development, and become proficient in data manipulation using libraries like Pandas and Num Py.· Natural Language Processing (NLP): Dive into the world of NLP, mastering techniques for text processing, feature extraction, and leveraging powerful libraries like NLTK and Spa Cy.· Deep Learning and Transformers: Understand the architecture of Transformer models, which are at the heart of many state-of-the-art AI applications. Discover the principles of deep learning and how to implement neural networks using TensorFlow and PyTorch.· Large Language Models (LL Ms): Gain insights into LL Ms, their training, and fine-tuning processes. Learn how to effectively use these models in various applications, from chatbots to content generation.· Retrieval-Augmented Generation (RAG): Explore the innovative concept of RAG, which combines retrieval techniques with generative models to enhance AI performance.· Prompt Engineering: Master the art of crafting effective prompts to improve the interaction with LL Ms and optimize the output for specific tasks.· Vector Databases: Discover how to implement and utilize vector databases for storing and retrieving high-dimensional data, a crucial skill in managing AI-generated content.The course culminates in a Capstone Project, where you will apply everything you've learned to solve a real-world problem using Generative AI te

Interested in Machine Learning and Deep Learning ? Then this course is for you!This course is about the fundamental concepts of machine learning, deep learning, reinforcement learning and machine learning. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking.In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. We will use Python with Sk Learn, Keras and TensorFlow. MACHINE LEARNING Linear Regressionunderstanding linear regression modelcorrelation and covariance matrixlinear relationships between random variablesgradient descent and design matrix approaches Logistic Regressionunderstanding logistic regressionclassification algorithms basicsmaximum likelihood function and estimationK-Nearest Neighbors Classifierwhat is k-nearest neighbour classifier?non-parametric machine learning algorithms Naive Bayes Algorithmwhat is the naive Bayes algorithm?classification based on probabilitycross-validation overfitting and underfitting Support Vector Machines (SV Ms)support vector machines (SV Ms) and support vector classifiers (SV Cs)maximum margin classifierkernel trick Decision Trees and Random Forestsdecision tree classifierrandom forest classifiercombining weak learners Bagging and

Welcome to the Captivating World of LLM Prompt Engineering!This course empowers you to unlock the true potential of Large Language Models (LL Ms), regardless of your experience level. Whether you're a seasoned professional or a curious beginner, this comprehensive program equips you with the skills to become a master of LLM prompt engineering.Master the Art of Crafting Powerful Prompts:Diverse Task Applications: Craft effective prompts tailored to various tasks, including generating informative summaries, creating captivating stories, or even translating languages, all through the power of well-designed prompts.Advanced Techniques Exploration: Move beyond the basics and delve into advanced concepts like iterative prompting, where you refine your prompt based on the LLM's initial output. Additionally, explore few-shot learning, allowing you to achieve impressive results even with limited data.Core LLM Concepts Demystified: Gain a solid understanding of fundamental LLM properties like statelessness and quantization. Explore how these properties impact prompt design and LLM behavior. Learn to identify and mitigate potential hallucinations in LLM outputs.Unleash LLM Capabilities Through Hands-on Learning:Code Walkthroughs Deepen Understanding: Go beyond theory with interactive code walkthroughs using Lamma 2 as a platform. Actively explore code examples to gain practical experience in setting up, configuring LL Ms, working with advanced models (e.g., quantized models), and leveraging specialized notebooks like AWQ for optimized workflows.Real-World Applications Solidify Skills: This course emphasizes the practical application of LLM prompt engineering. Learn how to tailor prompts to solve specific real-world problems, ensuring accurate and creative AI outputs. Translate your newfound knowledg

Hello there,Machine learning python, python, machine learning, Django, ethical hacking, python bootcamp, data analysis, machine learning python, python for beginners, data science, machine learning, django:Welcome to the “Python: Machine Learning, Deep Learning, Pandas, Matplotlib” course. Python, Machine Learning, Deep Learning, Pandas, Seaborn, Matplotlib, Geoplotlib, Num Py,  Data Analysis, TensorFlow Python instructors on Udemy specialize in everything from software development to data analysis and are known for their effective, friendly instruction for students of all levels.Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. Whether you’re a marketer, video game designer, or programmer, this course is here to help you apply machine learning to your work.In this course, we will learn what is Deep Learning and how does it work.This course has suitable for everybody who is interested in Machine Learning and Deep Learning concepts in Data Science.First of all, in this course, we will learn some fundamental stuff of Python and the Numpy library. These are our first steps in our Deep Learning journey. After then we take a little trip to Machine Learning Python history. Then we will arrive at our next stop. Machine Learning in Python Programming. Here we learn the machine learning concepts, machine learning a-z workflow, models and algorithms, and what is neural network concept. After then we arrive at our next stop. Artificial Neural network. And now our journey becomes an adventure. In this adventure we'll enter the Keras world then we exit the TensorFlow world. Then we'll try to under

Welcome to "ChatGPT for Pros: Generative AI and Prompt Engineering"—a comprehensive, beginner-friendly course designed to help you unlock the full potential of ChatGPT and Generative AI. Whether you're an aspiring entrepreneur, freelancer, content creator, or professional, this course will teach you how to effectively use AI tools to enhance your productivity and generate income across multiple industries.In this course, you'll begin with the basics of ChatGPT, learning how to set up your account and navigate the interface. You'll explore the foundational concepts of Generative AI, understanding how it works and how it can be applied to streamline your workflows. As you progress, you’ll dive deep into Prompt Engineering, discovering how to craft effective and specific prompts to maximize the quality of ChatGPT’s output.You’ll learn how to harness the power of ChatGPT for diverse applications such as content creation, including writing blogs, e Books, and social media posts, as well as crafting compelling copy for marketing campaigns. The course also covers using Generative AI for freelancing, where you'll discover how to optimize your profile on platforms like Linked In, design a portfolio website, and find clients using AI-driven tools. Additionally, you will learn how to create passive income through blogging and digital product creation, allowing you to generate revenue with minimal ongoing effort.We also explore how to integrate Generative AI into business automation, showing you how to automate tasks like social media scheduling, email marketing, and customer support, freeing up more of your time to focus on growth. By the end of this course, you’ll be equipped with the skills to use ChatGPT to drive business success, wh

Sentiment analysis and machine translation models are used by millions of people every single day. These deep learning models (most notably transformers) power different industries today. With the creation of much more efficient deep learning models, from the early 2010s, we have seen a great improvement in the state of the art in the domains of sentiment analysis and machine translation.In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step approach. We shall start by understanding how to process text in the context of natural language processing, then we would dive into building our own models and deploying them to the cloud while observing best practices. We are going to be using TensorFlow 2 (the world's most popular library for deep learning, built by Google) and Hugging Face You will learn:The Basics of TensorFlow (Tensors, Model building, training, and evaluation).Deep Learning algorithms like Recurrent Neural Networks, Attention Models, Transformers, and Convolutional neural networks.Sentiment analysis with RNNs, Transformers, and Hugging Face Transformers (Deberta)Transfer learning with Word2vec and modern Transformers (GPT, Bert, ULmfit, Deberta, T5...)Machine translation with RNNs, attention, transformers, and Hugging Face Transformers (T5)Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)If you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals!This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much em

Unlock the Power of Data Science Skills In today's data-driven world, the ability to harness and interpret data is not just a valuable skill but a crucial advantage. Whether you're an aspiring data scientist, a seasoned professional looking to expand your skill set, or an entrepreneur aiming to leverage data for strategic decisions, our comprehensive course on data science offers a transformative learning experience.Course Overview Our course begins with a foundational exploration of data science, introducing you to its principles and importance in various industries. You'll delve into the distinctions between data science, data engineering, and data analysis, gaining a clear understanding of their respective roles and applications. Through real-world case studies and examples, you'll discover how data science drives innovation and impacts decision-making processes across different sectors.Essential Tools and Technologies To equip you with the tools needed for effective data analysis, the course covers essential programming languages such as Python and R. Whether you're manipulating data with Pandas, performing numerical operations with Num Py, or creating insightful visualizations with Matplotlib and Seaborn, you'll develop a versatile skill set that forms the backbone of data science projects.Practical Skills DevelopmentA significant focus of the course is hands-on learning.  You'll gain practical experience in gathering, cleaning, and analyzing data from diverse sources.  You'll hone your ability to transform raw data into actionable insights that drive business decisions.Environment Setup and Best Practices Navigating the data science environment can be daunting, especially for beginners. That's why we guide you through the setup of Python and Jupyter Notebook on both Windows and macOS, ensuring you're equipped with the right tools from the start. You'll learn to create and manage virtual environments, enhancing your abili

Unlock the power of Generative AI, ChatGPT, and Prompt Engineering to work smarter, faster, and more creatively. This hands-on course teaches you how to use today’s most effective AI tools and automation techniques to improve productivity across writing, design, video creation, voice, data analysis, and web-related tasks.You will learn practical prompt engineering techniques and how to apply them correctly with industry-leading AI platforms. Instead of focusing on theory, this course emphasizes real-world use cases, workflows, and automation strategies that professionals, creators, and businesses use every day.Throughout the course, you will work with tools such as ChatGPT, Canva Magic Studio, Runway ML, Eleven Labs, Notion AI, DALL·E 3, and other modern generative AI platforms. You will understand not only how these tools work, but how to choose the right tool, design effective prompts, and integrate AI into your daily workflow.What Makes This Course Different Clear and practical prompt engineering explained in a simple, beginner-friendly way Hands-on, project-based lessons focused on real productivity gains Coverage of multiple AI categories in one complete course Practical automation and workflow strategies for work and business Tool comparisons and best practices to help you get better results faster Who This Course Is For This course is designed for:Professionals who want to save time and increase efficiency using AI Content creators and marketers looking to scale output with AI tools Entrepreneurs and freelancers aiming to use AI for business growth Beginners who want a clear and practical introduction to Generative AI No prior AI experience is required. Everything is taught step by step using real examples and prac

In this course, you will get to understand the foundational concepts that underlie the supervised machine-learning process. You will get to understand complex topics such as:Exploratory Data Analysis, Data Transformation and Feature Scaling, Evaluation Metrics, Algorithms, trainers, and models,Underfitting and Overfitting, Cross-validation, Regularization, and much more You will see these concepts come alive by doing a practical machine-learning exercise, rather than by looking at presentations. We will be using a non-cloud-based machine-learning tool called Model Builder, inside of Visual Studio. There will be zero coding involved (except for the very last lesson). But even though there is little coding involved, you will still get a very detailed understanding of complex machine-learning concepts.This course requires you to have at least some theoretical exposure to the concepts of supervised and unsupervised machine learning. This course is designed to build on a basic, theoretical understanding of machine learning by doing a practical machine-learning exercise. The concepts taught in this course are foundational and will be relevant in the future, regardless of what machine learning platform or programming language you use. In the process, you will also get some exposure to Visual Studio, code projects, solutions, and the Microsoft Machine Learning ecosystem. But that is just a side benefit. This course focuses on machine learning itself, not the tools that are used.If you've already done any kind of machine learning or trained a model, this course might be too basic for you. This course may contain foundational knowledge that you may not have been taught before, but please be aware that this course is geared toward beginner and intermediate-level AI enthusiasts.

Interested in the field of Machine Learning? Then this course is for you! This course has been designed by experts so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative field of ML. This course is fun and exciting, but at the same time we dive deep into Machine Learning. we will be covering the following topics in a well crafted way: Tensors and TensorFlow on the Cloud - what neural networks, Machine learning and deep learning really are, how neurons work and how neural networks are trained. - Datalab, Linear regressions, placeholders, variables, image processing, MNIST, K- Nearest Neighbors, gradient descent, softmax and more Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. Course Overview Module 1- Introduction Gcloud Introduction Labs Module 2 - Hands on GCP Labs Module 2-Datalab Module 3-Machine Learning & TensorFlow Introduction to Machine Learning, Typical usage of Mechine Learning, Types, The Mechine Learning block diagram, Deep learning & Neural Networks, Labels, Understanding Tenser Flow, Computational Graphs, Tensors, Linear regression , Placeholders & variables, Image processing in TensorFlow, Image as tensors, M-NIST – Introduction, K-nearest neighbors Algorithm, L1 distance, Steps in K- nearest neighbour implementation, Neural Networks i

Machine learning and Deep Learning have been gaining immense traction lately, but until now Java Script developers have not been able to take advantage of it due to the steep learning curve involved in learning a new language. Here comes a browser based Java Script library, TensorFlow.js to your rescue using which you can train and deploy machine learning models entirely in the browser. If you’re a Java Script developer who wants to enter the field ML and DL using TensorFlow.js, then this course is for you.This course takes a step by step approach to teach you how to use Java Script library, TensorFlow.js for performing machine learning and deep learning on a day-to-day basis. Beginning with an introduction to machine learning, you will learn how to create machine learning models, neural networks, and deep learning models with practical projects. You will then learn how to include a pre-trained model into your own web application to detect human emotions based on pictures and voices. You will also learn how to modify a pre-trained model to train the emotional detector from scratch using your own data.Towards the end of this course, you will be able to implement Machine Learning and Deep Learning for your own projects using Java Script and the TensorFlow.js library.Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Arish Ali started his machine learning journey 5 years ago by winning an all-India machine learning competition conducted by IISC and Microsoft. He was a data scientist at Mu Sigma, one of the biggest analytics firms in India. He has worked on some cutting-edge problems involved in multi-touch attribution modeling, market mix modeling, and Deep Neural Networks. He has also been an Adjunct faculty for Predictive Business Analytics at the Bridge School of Management, which along with Northwestern University (SPS) offers a course in

Unlock the creative potential of Generative Adversarial Networks (GANs) and Neural Style Transfer in this hands-on course, designed to guide you through the most advanced techniques in AI-driven image generation and art creation. Using TensorFlow, we will dive into the core concepts of GANs and explore their various architectures, providing you with practical skills to implement them from scratch.In the first half of the course, you'll master GANs by implementing several popular architectures:Vanilla GANs: Understand the basics of GANs and how the generator and discriminator interact.DCGAN (Deep Convolutional GANs): Learn how to generate high-quality images using convolutional layers.Wasserstein GANs (WGAN): Discover how WGAN improves stability and reduces mode collapse in GANs training.Conditional GANs (CGAN): Create conditional models that allow for more control over generated images.Pix2Pix GANs: Learn how to convert images from one domain to another, such as turning sketches into photos.Cycle GANs: Master the art of unpaired image-to-image translation, perfect for tasks like photo enhancement or style transfer.In the second part of the course, we delve into the fascinating world of Neural Style Transfer:Vanilla Neural Style Transfer: Learn how to blend the content of one image with the style of another.Feed Forward Style Transfer: Understand the advantages of using fast neural networks for style transfer.Arbitrary Style Transfer: Generate any artistic style on any content image, enabling limitless creativity.GauGAN: Create realistic images using a simple sketch, by applying a powerful

Do you want to become an AWS Machine Learning Engineer Using Sage Maker in 30 days?Do you want to build super-powerful production-level Machine Learning (ML) applications in AWS but don’t know where to start?Are you an absolute beginner and want to break into AI, ML, and Cloud Computing and looking for a course that includes everything you need?Are you an aspiring entrepreneur who wants to maximize business revenues and reduce costs with ML but don’t know how to get there quickly and efficiently?Do you want to leverage ChatGPT as a programmer to automate your coding tasks?If the answer is yes to any of these questions, then this course is for you!Machine Learning is the future one of the top tech fields to be in right now! ML and AI will change our lives in the same way electricity did 100 years ago. ML is widely adopted in Finance, banking, healthcare, transportation, and technology. The field is exploding with opportunities and career prospectsAWS is the one of the most widely used cloud computing platforms in the world and several companies depend on AWS for their cloud computing purposes. AWS Sage Maker is a fully managed service offered by AWS that allows data scientist and AI practitioners to train, test, and deploy AI/ML models quickly and efficiently.This course is unique and exceptional in many ways, it includes several practice opportunities, quizzes, and final capstone projects. In this course, students will learn how to create production-level ML models using AWS. The course is divided into 8 main sections as follows:Section 1 (Days 1 – 3): we will learn the following: (1) Start with an AWS and Machine Learning essentials “starter pack” that includes key AWS services such as Simple Storage Service (S3), Elastic Compute Cloud (EC2), Identity and Access Management (IAM) an

In today's data-driven world, the ability to harness and interpret data is not just a valuable skill but a crucial advantage. Whether you're an aspiring data scientist, a seasoned professional looking to expand your skill set, or an entrepreneur aiming to leverage data for strategic decisions, our comprehensive course on data science offers a transformative learning experience.Course Overview Our course begins with a foundational exploration of data science, introducing you to its principles and importance in various industries. You'll delve into the distinctions between data science, data engineering, and data analysis, gaining a clear understanding of their respective roles and applications. Through real-world case studies and examples, you'll discover how data science drives innovation and impacts decision-making processes across different sectors.Essential Tools and Technologies To equip you with the tools needed for effective data analysis, the course covers essential programming languages such as Python . Whether you're manipulating data with Pandas, performing numerical operations with Num Py, or creating insightful visualizations with Matplotlib and Seaborn, you'll develop a versatile skill set that forms the backbone of data science projects.Practical Skills DevelopmentA significant focus of the course is hands-on learning.  You'll gain practical experience in gathering, cleaning, and analyzing data from diverse sources. Through interactive exercises and projects, you'll hone your ability to transform raw data into actionable insights that drive business decisions.Environment Setup and Best Practices Navigating the data science environment can be daunting, especially for beginners. That's why we guide you through the setup of Python and Jupyter Notebook on both Windows and macOS, ensuring you're equipped with the right tools from the start. You'll learn to create and manage virtual environments, enhancing your ability to work efficiently and mai

Deep Learning is a hot topic today! This is because of the impact it's having in several industries. One of fields in which deep learning has the most influence today is Computer Vision.Object detection, Image Segmentation, Image Classification, Image Generation & People Counting To understand why Deep Learning based Computer Vision is so popular; it suffices to take a look at the different domains where giving a computer the power to understand its surroundings via a camera has changed our lives.Some applications of Computer Vision are:Helping doctors more efficiently carry out medical diagnosticsenabling farmers to harvest their products with robots, with  the need for very little human intervention,Enable self-driving cars Helping quick response surveillance with smart CCTV systems, as the cameras now have an eye and a brain Creation of art  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:  * Le Net5,  * Alex Net,  * VGG-16,  * Res Net,  * Inception,  * The lastest state of art Vision Transformer (ViT)- Many practical applications using famous datasets and sources such as:  * Covid19 on X-Ray images,  * CIFAR10,  * Fashion MNIST,  * BCCD,  * COCO dataset,  * Open Images Dataset V6 through Voxel Fifty

This course focuses on the fundamentals of Data Science, Machine learning, and deep learning in the beginning and with the passage of time, the content and lectures become advanced and more practical. But before everything, the introduction of python is discussed. Python is one of the fastest-growing programming languages and if we specifically look from the perspective of Data Science, Machine learning and deep learning, there is no other choice then “python” as a programming language.First of all, there is a crash course on python for those who are not very good with python and then there is an exercise for python that is supposed to be solved by you but if you feel any difficulty in solving the exercise, the solution is also provided.Then we moved on towards the Data Science and we start from data parsing using Scrapy then the data visualizations by using several libraries of python and finally we end up learning different data preprocessing techniques. And in the end, there is a complete project that we’ll do together.After that, we’ll be learning a few classical and a few advanced machine learning algorithms. Some of them will be implemented from scratch and the others will be implemented by using the builtin libraries of python. At the end of every algorithm, there will be a mini-project.Finally, Deep learning will be discussed, the basic structure of an artificial neural network and it’s the implementation in TensorFlow followed by a complete deep learning-based project. And in the end, some hyperparameter tuning techniques will be discussed that’ll improve the performance of the model.About The Instructor:Below is an introduction to Mr. Sajjad Mustafa, the instructor of this course.He an expert in Web

Have you ever watched AI automatically classify images or detect spam and thought, “I wish I could do that”? Have you ever wondered how a spam filter works? Or do you want to master Deep Learning in a hands-on way? With this course, you’ll learn how to build and deploy your own deep learning models in just 15 days - gaining practical, hands-on experience every step of the way.Why This Course?From day one, you’ll get comfortable with the essential concepts that power modern AI. No fluff, no endless theory - you'll learn by building real-world projects like Spam filters, or image detections. By the end, you won’t just know what neurons and neural networks are - you’ll be able to train, refine, and apply them to projects that truly matter.Who Is This Course For?Absolute beginners eager to break into the world of AI and deep learning.Data enthusiasts who want to strengthen their portfolios with hands-on projects.Developers and data scientists looking to deepen their PyTorch and model deployment skills.Anyone who craves a clear roadmap to mastering deep learning, one day at a time.What Makes This Course Unique?Day-by-Day Progression: Follow a structured, 15-day plan that ensures you never feel lost or overwhelmed.Real-World Projects: Predict used car prices, detect spam in SMS, classify handwritten digits, recognize fashion items—all using deep learning techniques.Modern Tools & Frameworks: Master industry-standard tools like PyTorch and dive into CNNs, transfer learning with 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

Welcome to the Full Stack Data Science &amp; Machine Learning Boot Camp Course, the only course you need to learn Foundation skills and get into data science.At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here's why:The course is taught by the lead instructor at the PwC, India's leading in-person programming bootcamp.In the course, you'll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix.This course doesn't cut any corners, there are beautiful animated explanation videos and real-world projects to build.The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback.To date, I’ve taught over 10000+ students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup.You'll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp.We'll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional.The course includes over 40+ hours of HD video tutorials and builds your programming knowledge while solving real-world problems.In the curriculum, we cover a large number of important data science and machine learning topics, such as:<

This course provides&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.

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

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.

This course teaches big ideas in machine learning like how to build and evaluate predictive models. This course provides an intro to clustering in R from a machine learning perspective.This online machine learning course is perfect for those who have a solid basis in R and statistics but are complete beginners with machine learning. You’ll get your first intro to machine learning.After learning the true fundamentals of machine learning, you'll experiment with the techniques that are explained in more detail. By the end, you'll be able to learn and build a decision tree and to classify unseen observations with k-Nearest Neighbors.Also, you'll be acquainted with simple linear regression, multi-linear regression, and k-Nearest Neighbors regression.This course teaches the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for optimal performance, how to preprocess data for better results, and much more.At the end of this course, our machine learning and data science video tutorials, you’ll have a great understanding of all the main principles.Details of the course:Module 01: Basics of R tool In this video, we are going to install r programming with rstudio in Windows Platform.Lab 01 R Installation and Concepts In this lab, we are going to learn about how we can install R Programing in Windows and learn about its several key concepts that are necessary for Programming in R.Video 2_R Programming Concepts In this video, we are going to learn the necessary concepts of RProgramming.Video 3_R Progrming Computations In this tutorial, we will be learning about several mathematical algorithms and computations.Lab 02 R P

Python, Java, Py Charm,&nbsp;Android Studio and MNIST.&nbsp;Learn to code and build apps! Use machine learning models in hands-on projects. &nbsp;A wildly successful Kickstarter funded this course Explore machine learning concepts.&nbsp;Learn how&nbsp;to use TensorFlow 1.4.1 to build, train, and test&nbsp;machine learning models.&nbsp;We explore Python 3.6.2&nbsp;and Java 8&nbsp;languages, and how to use Py Charm&nbsp;2017.2.3 and Android Studio 3 to build apps.&nbsp;A machine learning framework for everyone If you want to build sophisticated and intelligent mobile apps or simply want to know more about how machine learning works in a mobile environment, this course is for you.Be one of the first There are next to&nbsp;no&nbsp;courses on big platforms&nbsp;that focus on&nbsp;mobile&nbsp;machine learning in particular. All of them focus specifically on machine learning for a desktop or laptop environment.We&nbsp;provide clear, concise explanations at each step along the way so that viewers can not only replicate, but also understand and&nbsp;expand&nbsp;upon what I teach. Other&nbsp;courses don’t do a great job of explaining exactly what is going on at each step in the process and why we choose to build models the way we do.&nbsp;No prior knowledge is required We will teach you all you need to know about the languages, software and technologies we use. If you have lots of experience building machine learning apps, you may find this course a little slow because it’s designed for beginners.Jump into a field that has more&nbsp;demand&nbsp;than supply Machine learning changes everything. It’s bringing us self-driving cars, facial recognition and artificial intelligence. And the best part is: anyone can create such innovations."This course is GREA

Welcome to the Complete Guide to TensorFlow for Deep Learning with Python! This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure TensorFlow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way! This course covers a variety of topics, including Neural Network 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

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

In today's data-driven world, the ability to harness and interpret data is not just a valuable skill but a crucial advantage. Whether you're an aspiring data scientist, a seasoned professional looking to expand your skill set, or an entrepreneur aiming to leverage data for strategic decisions, our comprehensive course on data science offers a transformative learning experience.Course Overview Our course begins with a foundational exploration of data science, introducing you to its principles and importance in various industries. You'll delve into the distinctions between data science, data engineering, and data analysis, gaining a clear understanding of their respective roles and applications. Through real-world case studies and examples, you'll discover how data science drives innovation and impacts decision-making processes across different sectors.Essential Tools and Technologies To equip you with the tools needed for effective data analysis, the course covers essential programming languages such as Python . Whether you're manipulating data with Pandas, performing numerical operations with Num Py, or creating insightful visualizations with Matplotlib and Seaborn, you'll develop a versatile skill set that forms the backbone of data science projects.Practical Skills DevelopmentA significant focus of the course is hands-on learning.&nbsp; &nbsp;You'll gain practical experience in gathering, cleaning, and analyzing data from diverse sources. Through interactive exercises and projects, you'll hone your ability to transform raw data into actionable insights that drive business decisions.Environment Setup and Best Practices Navigating the data science environment can be daunting, especially for beginners. That's why we guide you through the setup of Python and Jupyter Notebook on both Windows and macOS, ensuring you're equipped with the right tools from the start. You'll learn to create and manage virtual environments, enhancing your ability to work efficiently a

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

Hello and welcome to the Machine Learning with STATA&nbsp;course. Machine Learning is influencing our daily lives and is one of the most significant aspects of technological advancements. The goal of this course is to provide you with the most up-to-date Machine Learning methodologies using STATA . It will teach you how to think about data science and machine learning in a new way. This is an excellent approach to begin a career in Machine Learning because you will learn some fundamental principles and receive practical experience. I'm thrilled to share what I know about Machine Learning using STATA&nbsp;with you. I assure you that it will be well worth your time and effort, and that you will gain a vital skill.Based on our research this is the only course that uses&nbsp; STATA&nbsp;to apply Machine Learning Models in Credit Risk Scenario. Because we know that many of you are already familiar with STATA&nbsp;or want to be familiar, we chose it as our platform. From the beginning to the finish of the course, we will start from scratch and work together to build new abilities. In this course, we will work together to create a complete data science project utilizing Credit Risk Data from start to finish. For this course, we have information on around 40,000 consumers, including their level of education, age, marital status, where they live, if they own a home, and other pertinent information. We'll get our hands filthy with these numbers and dig deep into them, and you'll be able to practice on your own. Additionally, you will have access to essential resources like as lectures, homework, quizzes, slides, and a literature analysis on modeling methodologies. Let's see what the course structure looks like right now!

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

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 :)

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.

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.

Java Server Pages (JSP) is a server-side programming technology that enables the creation of dynamic, platform-independent method for building Web-based applications. JSP have access to the entire family of Java AP Is, including the JDBC API to access enterprise databases. This tutorial will teach you how to use Java Server Pages to develop your web applications in simple and easy steps.Why to Learn JSP?Java Server Pages often serve the same purpose as programs implemented using the Common Gateway Interface (CGI). But JSP offers several advantages in comparison with the CGI.Performance is significantly better because JSP allows embedding Dynamic Elements in HTML Pages itself instead of having separate CGI files.JSP are always compiled before they are processed by the server unlike CGI/Perl which requires the server to load an interpreter and the target script each time the page is requested.Java Server Pages are built on top of the Java Servlets API, so like Servlets, JSP also has access to all the powerful Enterprise Java AP Is, including JDBC, JNDI, EJB, JAXP, etc.JSP pages can be used in combination with servlets that handle the business logic, the model supported by Java servlet template engines.Finally, JSP is an integral part of Java EE, a complete platform for enterprise class applications. This means that JSP can play a part in the simplest applications to the most complex and demanding.Audience This tutorial has been prepared for the beginners to help them understand basic functionality of Java Server Pages (JSP) to develop your web applications. After completing this tutorial you will find yourself at a moderate level of expertise in using JSP from where you can take yourself to next levels.

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

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

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.

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

Disclaimer:The second of this course demonstrates techniques using Jupyter Notebooks from Anaconda.&nbsp; You are welcome to follow along (however), it is not required to do these exercises to complete this course. If you are a Udemy Business user, please check with your employer before downloading software.Welcome!: Thank you all for the huge response to this emerging course!&nbsp;&nbsp;We are delighted to have over 20,000 students in over 160 different countries.&nbsp; I'm genuinely touched by&nbsp;the overwhelmingly positive and thoughtful reviews.&nbsp; It's such a privilege to share and introduce this important topic with everyday people in&nbsp;a clear and understandable way.&nbsp;I'm also excited to announce that I&nbsp;have created real closed captions for all course material, so weather you need them due to a hearing impairment, or find it easier to follow long (great for ESL&nbsp;students!)... I've got you covered.&nbsp;Most importantly: To make this course "real", we've expanded.&nbsp; In November of 2018, the course went from 41 lectures and 8 sections, to 62 lectures and 15 sections!&nbsp;&nbsp;We hope you enjoy the new content!&nbsp;&nbsp; Unlock the secrets of understanding Machine Learning for Data Science!In this introductory course, the “Backyard Data Scientist” will guide you through wilderness of Machine Learning for Data Science.&nbsp; Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the “techno sphere around us”, why it’s important now, and how it will dramatically change our world today and for days to come. Our exotic journey will include the core concepts of:The train wreck definition of computer science and one that will actually instead make sense.&nbsp; An explanation of data that will have you seeing data everywhere t

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