Building Machine Learning Solutions with TensorFlow.js

Machine learning and deep learning are powering some of the most groundbreaking applications of the current era. However, up until recently, JavaScript was not considered the go-to language for machine learning model development and deployment, despite being one of the most popular languages in the world. TensorFlow.js now allows JavaScript developers to extend their skills to build, train, and deploy machine learning and deep learning models. In this course, Building Machine Learning Solutions with TensorFlow.js 2, you'll learn about the TensorFlow.js ecosystem and how to set it up on the client-side in the browser and on the server-side with Node.js. First, you'll discover how to use the environment to build an end-to-end machine learning application that uses natural language processing (NLP) under the hood to detect toxic elements in unstructured text. Next, you'll learn how to import and process data, build, train, and export a model, and finally predict using the trained model. Finally, you'll explore how to use existing models trained in Python on the client-side using TensorFlow.js, and even retrain the pre-trained model using transfer learning. By the end of this course, you'll have the skills and knowledge of TensorFlow.js to build, train, and deploy machine learning and deep learning models on the client-side, as well as on the server-side that can power sophisticated applications.