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Informationen zum Autor 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. Klappentext HIGHLIGHT The first and only book on Apache Mahout! an open source tool for leveraging machine learning techniques in large-scale applications. DESCRIPTION To benefit from prior experience in the use of a website! machine learning techniques are increasingly used. The Apache Mahout project is focused on three types of machine learning that are of particular interest to modern web developers-recommendation systems! classification! and clustering. Through real-world examples! Mahout in Action introduces the sorts of problems that these techniques are appropriate for! and then illustrates how Mahout can be applied to solve them. It places particular focus on issues of scalability! and how to apply these techniques at very large scale with the Apache Hadoop framework. KEY POINTS This book assumes familiarity with Java! and some basic grounding in machine learning techniques. F . First and only book devoted to Apache Mahout F . Practical insights from industry practitioners F . Real-world examples F . Discussion of large-scale implemetation with Hadoop Zusammenfassung 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. 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 followi...
