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)

Part of speech tagging

A part of speech (POS) tagger analyzes a piece of text, such as a sentence, and determines each individual word's POS in the context of the sentence. The only way to accomplish this is with a dictionary lookup, so it is not an algorithm that can be developed from first principles alone.

A great use case for POS tagging is intent extraction from commands. For instance, when you say Siri, please order me a pizza from John's pizzeria, the AI system will tag the command with parts of speech in order to extract the subject, verb, object, and any other relevant details from the command.

Additionally, POS tagging is often used as a supporting tool for other NLP operations. Topic extraction, for instance, makes heavy use of POS tagging in order to separate people, places, and topics from verbs and adjectives.

Keep in mind that POS tagging is never perfect...