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.

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.

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.

Owners of a Manning pBook purchased anywhere in the world can download a free eBook from manning.com at any time. They can do so multiple times and in any or all formats available (PDF, ePub or Kindle). To do so, customers must register their printed copy on Manning's site by creating a user account and then following instructions printed on the pBook registration insert at the front of the book.

• 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

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

Sean Owen has been a practicing software engineer for 9 years, most recently at Google, where he helped build and launch Mobile Web search. He joined Apache's Mahout machine learning project in 2008 as a primary committer and works as a Mahout consultant.

Robin Anil joined Apache's Mahout project as a Google Summer of Code student in 2008 and contributed to the Classifier and Frequent Pattern Mining packages with algorithms that run on the Hadoop Map/Reduce platform. Since 2009, he has been a committer at Mahout and works as a full-time Software Engineer at Google.

Ted Dunning is Chief Application Architect at MapR Technologies and committer and PMC member for the Apache Mahout project. He contributing to the Mahout clustering, classification and matrix decomposition algorithms. He was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems, and built fraud detection systems for ID Analytics.

Ellen Friedman is an experienced writer with a doctorate in biochemistry. In addition to a research career, she has written on a wide range of scientific and technical topics including molecular biology, medicine and earth science.