Free Mahout In Action Ebooks Online
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 , 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 InsideUse group data to make individual recommendations Find logical clusters within your data Filter and refine with on-the-fly classification Free audio and video extrasTable of ContentsMeet Apache Mahout PART 1 RECOMMENDATIONS Introducing recommenders Representing recommender data Making recommendations Taking recommenders to production Distributing recommendation computations PART 2 CLUSTERING Introduction to clustering Representing data Clustering algorithms in Mahout Evaluating and improving clustering quality Taking clustering to production Real-world applications of clustering PART 3 CLASSIFICATION Introduction to classification Training a classifier Evaluating and tuning a classifier Deploying a classifier Case study: Shop It To Me
Paperback: 416 pages
Publisher: Manning Publications (October 17, 2011)
Language: English
ISBN-10: 1935182684
ISBN-13: 978-1935182689
Product Dimensions: 7.4 x 0.9 x 9.2 inches
Shipping Weight: 1.6 pounds (View shipping rates and policies)
Average Customer Review: 3.9 out of 5 stars See all reviews (15 customer reviews)
Best Sellers Rank: #509,790 in Books (See Top 100 in Books) #83 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Machine Theory #121 in Books > Textbooks > Computer Science > Algorithms #172 in Books > Computers & Technology > Databases & Big Data > Data Warehousing
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.
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