Apache Spark 2.x Machine Learning Cookbook (PDF)

Description

This book is for Scala developers with a fairly good exposure to and understanding of machine learning techniques, but lack practical implementations with Spark. A solid knowledge of machine learning algorithms is assumed, as well as hands-on experience of implementing ML algorithms with Scala. However, you do not need to be acquainted with the Spark ML libraries and ecosystem.

What You Will Learn

Get to know how Scala and Spark go hand-in-hand for developers when developing ML systems with Spark
Build a recommendation engine that scales with Spark
Find out how to build unsupervised clustering systems to classify data in Spark
Build machine learning systems with the Decision Tree and Ensemble models in Spark
Deal with the curse of high-dimensionality in big data using Spark
Implement Text analytics for Search Engines in Spark
Streaming Machine Learning System implementation using Spark

Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks.

This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.

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