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. Included is 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

1. Meet Apache Mahout

PART 1 RECOMMENDATIONS

2. Introducing recommenders
3. Representing recommender data
4. Making recommendations
5. Taking recommenders to production
6. Distributing recommendation computations

PART 2 CLUSTERING

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

PART 3 CLASSIFICATION

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

(Computer Bookshops Limited)

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Table of Contents
Meet Apache Mahout
PART 1 RECOMMENDATIONS

Introducing recommenders
Representing recommender data
Making recommendations
Taking recommenders to production
Distributing recommendation computations
PART 2 CLUSTERING

Introduction to clustering
Representing data
Clustering algorithms in Mahout
Evaluating and improving clustering quality
Taking clustering to production
Real-world applications of clustering
PART 3 CLASSIFICATION

Introduction to classification
Training a classifier
Evaluating and tuning a classifier
Deploying a classifier
Case study: Shop It To Me

(Computer Bookshops Limited)