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)

Types of machine learning models

While we have one example, there are many other examples, some of which we will touch on in the relevant chapters when we introduce each machine learning task.

However, we can broadly divide the preceding use cases and methods into two categories of machine learning:

  • Supervised learning: These types of models use labeled data to learn. Recommendation engines, regression, and classification are examples of supervised learning methods. The labels in these models can be user--movie ratings (for the recommendation), movie tags (in the case of the preceding classification example), or revenue figures (for regression). We will cover supervised learning models in Chapter 4, Building a Recommendation Engine with Spark, Chapter 6, Building a Classification Model with Spark, and Chapter 7, Building a Regression Model with Spark.
  • Unsupervised learning: When a model does not require labeled...