Practical Machine Learning in JavaScript

Build machine learning web applications without having to learn a new language. This book will help you develop basic knowledge of machine learning concepts and applications.

You’ll learn not only theory, but also dive into code samples and example projects with TensorFlow.js. Using these skills and your knowledge as a web developer, you’ll add a whole new field of development to your tool set. This will give you a more concrete understanding of the possibilities offered by machine learning. Discover how ML will impact the future of not just programming in general, but web development specifically.

Machine learning is currently one of the most exciting technology fields with the potential to impact industries from health to home automation to retail, and even art. Google has now introduced TensorFlow.js—an iteration of TensorFlow aimed directly at web developers. Practical Machine Learning in JavaScript will help you stay relevant in the tech industry with new tools, trends, and best practices.

WHAT YOU'LL LEARN

* Use the JavaScript framework for ML
* Build machine learning applications for the web
* Develop dynamic and intelligent web content

WHO THIS BOOK IS FOR

Web developers and who want a hands-on introduction to machine learning in JavaScript. A working knowledge of the JavaScript language is recommended.

CHARLIE GERARD is a Senior front-end developer at Netlify, a Google Developer Expert in Web Technologies, and a Mozilla Tech Speaker. She is passionate about exploring the possibilities of the web and spends her personal time building interactive prototypes using hardware, creative coding, and machine learning. She has been diving into ML in JavaScript for over a year and built a variety of projects. She’s excited to share what she’s learned and help more developers get started.

Chapter 1: Introduction to Machine Learning

• Definition

• Explanation of concepts

• Algorithms

• Examples of impact

Chapter 2: Basics of Tensorflow.js

• What is Tensorflow.js?

• Features

Chapter 3: Building an Image Classifier

• Using a pre-trained model

• Creating a custom model

• Saving and loading a model

Chapter 4: Building a Sentiment Analysis System

• Train a model with text data

• Create text-based ML applications

Chapter 5: Experimenting with Inputs

• Using ML with electronics data

• Using audio data

Chapter 6: Deploying Models

Chapter7: Ethics in AI