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)

Regression versus classification

Much of this book has been involved with classification tasks, where the objective of the analysis is to fit a data point to one of a number of predefined classes or labels. When classifying data, you are able to judge your algorithm's accuracy by comparing predictions to true values; a guessed label is either correct or incorrect. In classification tasks, you can often determine the likelihood or probability that a guessed label fits the data, and you typically choose the label with the maximum likelihood.

Let's compare and contrast classification tasks to regression tasks. Both are similar in that the ultimate goal is to make a prediction, informed by prior knowledge or data. Both are similar in that we want to create some kind of function or logic that maps input values to output values, and make that mapping function both as accurate...