Book Image

Hands-on Machine Learning with JavaScript

Book Image

Hands-on Machine Learning with JavaScript

Overview of this book

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.
Table of Contents (14 chapters)

Introduction to machine learning

In general, ML is the name we give to the practice of making computers learn without explicitly programming insights into the algorithm. The converse practice—that is, programming an algorithm with a set of instructions that it can apply to datasets—is often called heuristics. This is our first classification of algorithms: machine learning versus heuristic algorithms. If you are managing a firewall and are manually maintaining a blacklist of IP address ranges to block, you can be said to have developed a heuristic for your firewall. On the other hand, if you develop an algorithm that analyzes patterns in web traffic, infers from those patterns, and automatically maintains your blacklist, you can be said to have developed an ML approach to firewalls.

We can, of course, further subcategorize our ML firewall approach. If your algorithm...