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)

Other time-series analysis techniques

Regressions are a great starting point for analyzing continuous data, however, there are many other techniques one can employ when analyzing time-series data specifically. While regressions can be used for any continuous data mapping, time-series analysis is specifically geared toward continuous data that evolves over time.

There are many examples of time-series data, for instance:

  • Server load over time
  • Stock prices over time
  • User activity over time
  • Weather patterns over time

The objective when analyzing time-series data is similar to the objective in analyzing continuous data with regressions. We wish to identify and describe the various factors that influence the changing value over time. This section will describe a number of techniques above and beyond regressions that you can use to analyze time-series data.

In this section, we will...