Recommender systems are widely studied, and there are many approaches used, but there are two that are probably most prevalent: content-based filtering and collaborative filtering. Recently, other approaches, such as ranking models, have also gained in popularity. In practice, many approaches are hybrids, incorporating elements of many different methods into a model or combination of models.
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Machine Learning with Spark - Second Edition
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Machine Learning with Spark
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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)
Preface
Free Chapter
Getting Up and Running with Spark
Math for Machine Learning
Designing a Machine Learning System
Obtaining, Processing, and Preparing Data with Spark
Building a Recommendation Engine with Spark
Building a Classification Model with Spark
Building a Regression Model with Spark
Building a Clustering Model with Spark
Dimensionality Reduction with Spark
Advanced Text Processing with Spark
Real-Time Machine Learning with Spark Streaming
Pipeline APIs for Spark ML
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