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)

The mathematical perspective

Association rule learning assumes that you have a transactional database to learn from. This doesn't refer to any specific technology, but rather the concept of a database that stores transactions—the database can be an array in memory, an Excel file, or a table in your production MySQL or PostgreSQL instance. Since association rule learning was developed for products in supermarkets, the original transactional database was a list of items bought by each individual shopper on a given shopping trip—essentially an archive of receipts from the checkout aisle. However, a transactional database can be any list of items or events that occur during a single session, whether that session is a shopping trip, a website visit, or a trip to a doctor. For the time being, we'll consider the supermarket example. We'll discuss other uses...