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Product Description
Product Description
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.
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
- Meet Apache Mahout
- Introducing recommenders
- Representing recommender data
- Making recommendations
- Taking recommenders to production
- Distributing recommendation computations
- Introduction to clustering
- Representing data
- Clustering algorithms in Mahout
- Evaluating and improving clustering quality
- Taking clustering to production
- Real-world applications of clustering
- Introduction to classification
- Training a classifier
- Evaluating and tuning a classifier
- Deploying a classifier
- Case study: Shop It To Me
PART 1 RECOMMENDATIONS
PART 2 CLUSTERING
PART 3 CLASSIFICATION
About the Author
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.
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Mahout in Action is written and explained so well with simple real life explanations and definitely executable code that you can gather all the techniques you've heard/read about come right near your grasp. Just extend your arms and reach for that recommender or clusterer.
A big thanks to every Mahout contributor and double thanks to the authors.
Oh by the way! Order the book. At whatever price, this will save you hundreds of hours of reading and coding.
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 ([...]) to solve some of machine learning problems: making recommendations, data clustering & 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 & 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.
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