Mahout in Action

Mahout in Action

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 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 following instructions printed on the pBook registration insert at the front of the book.

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

Table of Contents

  1. Meet Apache Mahout
  2. PART 1 RECOMMENDATIONS
  3. Introducing recommenders
  4. Representing recommender data
  5. Making recommendations
  6. Taking recommenders to production
  7. Distributing recommendation computations
  8. PART 2 CLUSTERING
  9. Introduction to clustering
  10. Representing data
  11. Clustering algorithms in Mahout
  12. Evaluating and improving clustering quality
  13. Taking clustering to production
  14. Real-world applications of clustering
  15. PART 3 CLASSIFICATION
  16. Introduction to classification
  17. Training a classifier
  18. Evaluating and tuning a classifier
  19. Deploying a classifier
  20. Case study: Shop It To Me

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3 comments

  1. 12 of 13 people found the following review helpful
    5.0 out of 5 stars
    “In Action” Absolutely. Machine Learning text converted to usable code, October 20, 2011
    By 
    Gadget Monster (Sunnyvale, CA) –

    Verified Purchase(What’s this?)
    This review is from: Mahout in Action (Paperback)
    I have a large scale production code background and have been slowly getting deeper and deeper into recommenders, classification & clustering due to the nature of our business. The Data Mining textbooks have a very different objective, which is to cover every technique so that the person taking the class knows ins and outs of these.
    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.

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  2. 8 of 10 people found the following review helpful
    5.0 out of 5 stars
    Great introduction to Apache Mahout!, October 15, 2011
    By 
    Alexey Ott
    (REAL NAME)
      

    This review is from: Mahout in Action (Paperback)
    If you’re interested in large scale machine learning, then this book is for you.
    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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  3. 3 of 3 people found the following review helpful
    4.0 out of 5 stars
    Slightly dated, but still useful, December 26, 2012
    By 
    Sujit Pal “Sujit Pal” (Antioch, CA USA) –
    (REAL NAME)
      

    Verified Purchase(What’s this?)
    This review is from: Mahout in Action (Paperback)
    Apache Mahout is a very active, very useful and very successful open-source project whose focus is to provide machine learning algorithms on top of Hadoop. Because of the activity, however, this book is doomed to appear dated by the time you read it. The book can be useful if you are looking to get an overview of whats available in Mahout and general guidelines on how to use it. Since the project’s focus is on providing ready-to-run implementations that can be run from a command line, the book can also be useful for the “under the hood” information it provides about how to use Mahout as an API, ie, use the components as building blocks for your own ready-to-run big data ML application. However, I found (quite drastic at times) differences between the book version (0.6) and what I was using (0.7), both for script parameters as well as Java API. So be prepared to read the project wiki (for the script parameters) and the source code (for using as an API).

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