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 TechnologyA 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 BookThis 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.

What's InsideUse group data to make individual recommendationsFind logical clusters within your dataFilter and refine with on-the-fly classificationFree audio and video extrasTable of ContentsMeet Apache MahoutPART 1 RECOMMENDATIONSIntroducing recommendersRepresenting recommender dataMaking recommendationsTaking recommenders to productionDistributing recommendation computationsPART 2 CLUSTERINGIntroduction to clusteringRepresenting dataClustering algorithms in MahoutEvaluating and improving clustering qualityTaking clustering to productionReal-world applications of clusteringPART 3 CLASSIFICATIONIntroduction to classificationTraining a classifier Evaluating and tuning a classifierDeploying a classifierCase study: Shop It To Me