Practical Machine Learning with TensorFlow, Keras, and Pose Detection

Machine learning is no longer confined to backend servers and complex cloud environments. With modern frameworks, you can build and run intelligent models directly in the browser to detect motion, recognize patterns, and create interactive user experiences. This text-based course guides you from the absolute basics of machine learning theory to implementing real-time models. You will learn how to structure data, train neural networks, and deploy pre-trained models like PoseNet for human pose estimation. What you'll learn: - Understand foundational machine learning concepts and neural network architectures. - Build and train custom models using the high-level Keras API. - Prepare and preprocess data efficiently for training. - Implement transfer learning to adapt existing models to new tasks. - Deploy models to the web browser using TensorFlow.js. - Configure real-time pose estimation and motion tracking with PoseNet. The course begins with essential terminology and core mathematical concepts before moving into hands-on code walkthroughs. You will progress from training simple classifiers in Python to running interactive, real-time detection models directly in browser-based environments. Designed for beginners, this course requires only basic programming familiarity, with no prior machine learning experience needed. Start reading today to unlock the potential of web-based machine learning.