by Sean Owen, Robin Anil, Ted Dunning, Ellen Friedman
“This book doesn't provide deep coverage of theoretical foundations of machine learning (I would recommend to look to other books, like "Introduction to Machine Learning (Adaptive Computation and Machine Learning series)", "Machine Learning in Action" or "Programming Collective Intelligence: Building Smart Web 2.0 Applications", etc., if you want to get more background), but concentrates on explanation on how to use Apache Mahout (http://mahout.apache.org/) to solve some of machine learning problems: making recommendations, data clustering and classification.
For each of class of these problems, description starts with base things, and continues with more complex examples, including complete solutions, that could be easily adapted for your machine learning problems. All examples that come with book were checked with actual release of Apache Mahout (version 0.5).
Book is written in succinct, but understandable language and provides many code snippets that make understanding of topics much easier. Interesting solution in e-book version of Mahout in Action, is inclusion of audio and video snippets, that explains and/or show "hard places". There is also interesting description of one of Mahout's deployments in real world, where it's used in e-commerce.
So I recommend this book if you're interested in solving machine learning problems that works with very large data sets.”
Alex Ott wrote this review Saturday, October 15, 2011.
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