TensorFlow.js is a framework that enables you to create performant machine learning (ML) applications that run smoothly in a web browser. With this course, you will learn how to use TensorFlow.js to implement various ML models through an example-based approach. Starting with the basics, you’ll understand how ML models can be built on the web. Moving on, you will get to grips with the TensorFlow.js ecosystem to develop applications more efficiently. The course will then guide you through implementing ML techniques and algorithms such as regression, clustering, fast Fourier transform (FFT), and dimensionality reduction. You will later cover the Bellman equation to solve Markov decision process (MDP) problems and understand how it is related to reinforcement learning. Finally, you will explore techniques for deploying ML-based web applications and training models with TensorFlow Core. Throughout this ML course, you’ll discover useful tips and tricks that will build on your knowledge. By the end of this course, you will be equipped with the skills you need to create your own web-based ML applications and fine-tune models to achieve high performance.