Programming Books C Java PHP Python Learn more Browse Programming Books

Sorry, this item is not available in
Image not available for
Color:
Image not available

To view this video download Flash Player

 


or
Sign in to turn on 1-Click ordering
Sell Us Your Item
For a $4.56 Gift Card
Trade in
More Buying Choices
Have one to sell? Sell yours here
Tell the Publisher!
I'd like to read this book on Kindle

Don't have a Kindle? Get your Kindle here, or download a FREE Kindle Reading App.

Mahout in Action [Paperback]

Sean Owen , Robin Anil , Ted Dunning , Ellen Friedman
4.0 out of 5 stars  See all reviews (13 customer reviews)

List Price: $44.99
Price: $29.35 & FREE Shipping on orders over $35. Details
You Save: $15.64 (35%)
o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o
In Stock.
Ships from and sold by Amazon.com. Gift-wrap available.
Want it Tuesday, July 15? Choose One-Day Shipping at checkout. Details
Free Two-Day Shipping for College Students with Amazon Student

Formats

Amazon Price New from Used from
Paperback $29.35  
Shop the new tech.book(store)
New! Introducing the tech.book(store), a hub for Software Developers and Architects, Networking Administrators, TPMs, and other technology professionals to find highly-rated and highly-relevant career resources. Shop books on programming and big data, or read this week's blog posts by authors and thought-leaders in the tech industry. > Shop now

Book Description

October 17, 2011 1935182684 978-1935182689

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

Frequently Bought Together

Mahout in Action + Hadoop: The Definitive Guide + Programming Hive
Price for all three: $88.33

Buy the selected items together


Editorial Reviews

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.


Product Details

  • Paperback: 416 pages
  • Publisher: Manning Publications (October 17, 2011)
  • Language: English
  • ISBN-10: 1935182684
  • ISBN-13: 978-1935182689
  • Product Dimensions: 9.1 x 7.3 x 0.9 inches
  • Shipping Weight: 1.6 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (13 customer reviews)
  • Amazon Best Sellers Rank: #52,832 in Books (See Top 100 in Books)

More About the Author

Discover books, learn about writers, read author blogs, and more.

Customer Reviews

Most Helpful Customer Reviews
12 of 13 people found the following review helpful
Format:Paperback|Verified Purchase
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.
Comment | 
Was this review helpful to you?
8 of 10 people found the following review helpful
5.0 out of 5 stars Great introduction to Apache Mahout! October 15, 2011
Format: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.
Comment | 
Was this review helpful to you?
3 of 3 people found the following review helpful
4.0 out of 5 stars Slightly dated, but still useful December 26, 2012
Format:Paperback|Verified Purchase
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).
Comment | 
Was this review helpful to you?
7 of 9 people found the following review helpful
Format:Paperback|Verified Purchase
There's a very incomplete and incoherent feel to the clustering section of this book. Mahout as a command line program vs mahout as a library of routines. A bit of theory thrown in here and there.

First the book use 0.5 of mahout while version 0.7 is the current release. Much of the code no longer works from the examples. Keeping the example code updated on the site would be a huge plus.

A useful start would have been discussing theory in chapter 7. Instead the theory is discussed in chapter 9.
Chatper 7 is a mishmash of distance measures, similarity and examples.

A thorough explanation of the output produced by clusterdumper would have been useful. With some knowledge of the algorithm you can figure out what c and r are and the numbers assigned to the vectors are. But taking a simple example and showing the actually hand calculation would be very useful to someone totaly new to clustering .

I don't like to be overly critical, the book has some good information, but its much more difficult to extract it than it should be.
Was this review helpful to you?
1 of 1 people found the following review helpful
4.0 out of 5 stars very thorough explanations February 16, 2014
By Michael
Format:Paperback|Verified Purchase
I am brand new to learning algorithms, so I was worried about trying to read this book. First I tried reading some documentation on the Mahout website, but I felt like I was getting a run-around treatment there. Their website kept providing links to documentation that required already knowing how these learning algorithms worked. Eventually I gave this book a try.

I was delightfully surprised, this book covers a lot of the learning algorithms in thorough detail. It is great for people with no prior knowledge of how machine learning works, like I was. If you already understand some things about machine learning, you will probably get bored fast.

I did have a few gripes though:

I felt like the clustering chapters did a great job explaining the k-means algorithm, but just did a little hand-waving for the more advanced algorithms. For example, the explanation of the canopy algorithm did not make sense to me after reading it twice, and I feel like the Latent Dirichlet Analysis algorithm made no sense at all. I learned what these algorithms were good for, but still don't completely understand how they work under the hood. Perhaps they are just too complicated to explain in the book, maybe they belong in an appendix, I don't know.

I'm reading the Classification chapters now, and I must admit that it's a bit verbose. The authors are repeating themselves way too much in chapter 13. I think multiple authors contributed to chapter 13 without looking at each-other's work. On the plus side I feel like I understand it.

I have not tried doing anything yet, some other reviewer's said the examples are out of date.
Read more ›
Comment | 
Was this review helpful to you?
Most Recent Customer Reviews
5.0 out of 5 stars Excellent Place to Start
I found this book and the included examples to be an excellent starting point to gain exposure to the various facets of machine learning. Read more
Published 6 months ago by java coder
5.0 out of 5 stars Very Helpful Guide
I found this book to be a very helpful guide. It has a number of useful examples, and the explanations are clearly written. Read more
Published 13 months ago by Robert L McPherson
2.0 out of 5 stars Good overview, terrible examples
Mahout in Action (MiA) offers a nice introduction to Mahout and machine learning. MiA progresses at a good pace and provides nice background on Mahout's core abilities: recommender... Read more
Published 17 months ago by Ricardo A. Radaelli-Sanchez
3.0 out of 5 stars Problem with getting the Ebook
I bought this book online here, so that I can get the ebook and also the paperback copy of the book. Read more
Published 23 months ago by Sandeep
2.0 out of 5 stars Way too verbose
This book is way TOO verbose. I think at least 2/3 of the contents can be cut without affecting anything, which is in fact the general feeling I have for all "*** in action" series... Read more
Published on May 5, 2012 by Runpu Sun
5.0 out of 5 stars Great guide
This book has been very helpful implementing multiple machine learning algorithms. The examples are very good and do a great job helping me to understand the software.
Published on April 23, 2012 by Jonathan Maurer
5.0 out of 5 stars great book
This is a very well written book, good examples & very good for beginners. I would request the authors to keep revising the book as mahout upgrades to newer versions. Read more
Published on March 10, 2012 by Cyril A. Furtado
5.0 out of 5 stars Excellent
Lucidly written, great for noobs. I am not a software engineer and I started learning from Machine learning from scratch. And I totally got it!
Published on December 18, 2011 by whackjob
Search Customer Reviews
Search these reviews only

What Other Items Do Customers Buy After Viewing This Item?


Sell a Digital Version of This Book in the Kindle Store

If you are a publisher or author and hold the digital rights to a book, you can sell a digital version of it in our Kindle Store. Learn more

Forums

Topic From this Discussion
The right audience for this book
Thanks for sharing. It is very helpful. I am similar as you. I want to know more about Mahout, not the machine learning basics. Also your message confirms that an ebook is indeed included in the shipping. That answers another question of mine.
Sep 23, 2011 by dancer |  See all 2 posts
Have something you'd like to share about this product?
Start a new discussion
Topic:
First post:
Prompts for sign-in
 


Search Customer Discussions
Search all Amazon discussions


Look for Similar Items by Category