This is the code repository for Hands-on Machine Learning with JavaScript, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added advantage of machine learning research and support for JS libraries, JavaScript makes your browsers smarter than ever with the ability to learn patterns and reproduce them to become a part of innovative products and applications.

Hands-on Machine Learning with JavaScript presents various avenues of machine learning in a practical and objective way, and helps implement them using the JavaScript language. Predicting behaviors, analyzing feelings, grouping data, and building neural models are some of the skills you will build from this book. You will learn how to train your machine learning models and work with different kinds of data. During this journey, you will come across use cases such as face detection, spam filtering, recommendation systems, character recognition, and more. Moreover, you will learn how to work with deep neural networks and guide your applications to gain insights from data.

By the end of this book, you'll have gained hands-on knowledge on evaluating and implementing the right model, along with choosing from different JS libraries, such as NaturalNode, brain, harthur, classifier, and many more to design smarter applications.

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter01.

The code will look like the following:

var items = [1, 2, 3 ];
for (var index in items) {
var item = items[index];
…
}

If you haven't programmed in JS in a while, it would be best for you to give yourself a refresher before you get started. In particular, the examples in this book will use ES6/ES2015 syntax; I will give you a tour of the new syntax in the first chapter, but you may also want to become familiar with it on your own. If you don't have Node.js installed yet, you'll want to install that now. The examples in this book were written using Node.js version 9.6.0, though I expect most of the examples to work for any Node.js version greater than 8 and for Node.js version 10 as well. You will not need much education in math to get through this book, but I assume that you paid attention to your high school math courses. If you don't remember much probability, statistics, or algebra, you may want to refresh yourself on those topics, as they are prevalent in ML. While I have tried my best to avoid deep dives into advanced mathematical concepts, I will indeed have to present some in this book so that you at least get a level of comfort with math and the willingness to research some select mathematical concepts on your own.

• Hands-On Deep Learning with Apache Spark

• Hands-On Deep Learning with PyTorch

• Hands-On Reinforcement Learning with Python

Click here if you have any feedback or suggestions.