Mahout in Action

Summary

Mahout in Action is a hands-on introduction to machine learning with Apache Mahout. Following real-world examples, the book presents practical use cases and then illustrates how Mahout can be applied to solve them. Includes a free audio- and video-enhanced ebook.

About the Technology

A computer system that learns and adapts as it collects data can be really powerful. Mahout, Apache’s open source machine learning project, captures the core algorithms of recommendation systems, classification, and clustering in ready-to-use, scalable libraries. With Mahout, you can immediately apply to your own projects the machine learning techniques that drive Amazon, Netflix, and others.

About this Book

This book covers machine learning using Apache Mahout. Based on experience with real-world applications, it introduces practical use cases and illustrates how Mahout can be applied to solve them. It places particular focus on issues of scalability and how to apply these techniques against large data sets using the Apache Hadoop framework.

This book is written for developers familiar with Java – no prior experience with Mahout is assumed.

What’s Inside

• Use group data to make individual recommendations
• Find logical clusters within your data
• Filter and refine with on-the-fly classification
• Free audio and video extras

Table of Contents

Chapter 1 Meet Apache Mahout

Part 1 Recommendations
Chapter 2 Introducing recommenders
Chapter 3 Representing recommender data
Chapter 4 Making recommendations
Chapter 5 Taking recommenders to production
Chapter 6 Distributing recommendation computations

Part 2 Clustering
Chapter 7 Introduction to clustering
Chapter 8 Representing data
Chapter 9 Clustering algorithms in Mahout
Chapter 10 Evaluating and improving clustering quality
Chapter 11 Taking clustering to production
Chapter 12 Real-world applications of clustering

Part 3 Classification
Chapter 13 Introduction to classification
Chapter 14 Training a classifier
Chapter 15 Evaluating and tuning a classifier
Chapter 16 Deploying a classifier
Chapter 17 Case study: Shop It To Me

Appendix A JVM tuning
Appendix B Mahout math
Appendix C Resources