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Hands-on Machine Learning with JavaScript

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  • Get an overview of state-of-the-art machine learning
  • Understand the pre-processing of data handling, cleaning, and preparation
  • Learn Mining and Pattern Extraction with JavaScript
  • Build your own model for classification, clustering, and prediction
  • Identify the most appropriate model for each type of problem
  • Apply machine learning techniques to real-world applications
  • Learn how JavaScript can be a powerful language for machine learning

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.

  • Solve complex computational problems in browser with JavaScript
  • Teach your browser how to learn from rules using the power of machine learning
  • Understand discoveries on web interface and API in machine learning
Page Count 356
Course Length 10 hours 40 minutes
Date Of Publication 28 May 2018
Average and distance
Writing the k-means algorithm
Example 1 – k-means on simple 2D data
Example 2 – 3D data
k-means where k is unknown
Regression versus classification
Regression basics
Example 1 – linear regression
Example 2 – exponential regression
Example 3 – polynomial regression
Other time-series analysis techniques


Burak Kanber

Burak Kanber is an entrepreneur, software engineer, and the co-author of "Genetic Algorithms in Java". He earned his Bachelor's and Master's degrees in Mechanical Engineering from the prestigious Cooper Union in New York City, where he concentrated on software modeling and simulation of hybrid vehicle powertrains.

Currently, Burak is a founder and the CTO of Tidal Labs, a popular enterprise influencer marketing platform. Previously, Burak had founded several startups, most notably a boutique design and engineering firm that helped startups and small businesses solve difficult technical problems. Through Tidal Labs, his engineering firm, and his other consulting work, Burak has helped design and produce dozens of successful products and has served as a technical advisor to many startups.

Burak's core competencies are in machine learning, web technologies (specifically PHP and JavaScript), engineering (software, hybrid vehicles, control systems), product design and agile development. He's also worked on several interactive art projects, is a musician, and is a published engineer.