Book Image

Machine Learning with Spark - Second Edition

By : Rajdeep Dua, Manpreet Singh Ghotra
Book Image

Machine Learning with Spark - Second Edition

By: Rajdeep Dua, Manpreet Singh Ghotra

Overview of this book

This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML. Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML. By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.
Table of Contents (13 chapters)

Comparing algorithms supported by MLlib

In this section, we look at various algorithms supported by MLlib versions.

Classification

In 1.6, there are over 10 algorithms supported for classification, whereas when Spark MLversion 1.0 was announced, only 3 algorithms were supported.

Clustering

There has been quite a bit of investment in Clustering algorithms, moving from 1 algo support in 1.0.0 to supporting 6 implementations in 1.6.0.

Regression

Traditionally, regression...