book data
88 ratings,
4.05
average rating, 21 reviews
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published
August 16th 2007
by O'Reilly Media, Inc.
binding
Paperback, 358 pages
isbn
0596529325
(isbn13: 9780596529321)
description
Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates ...more
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other reviews (showing 1-20 of 241)
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avg 4.05
editions: all | this edition
editions: all | this edition
Read in October, 2007
This is a beginner's guide to machine learning techniques. In typical O'Reilly fashion, there's very little math but lots of code snippets. While you will learn some motivation for using various techniques, you won't be able to start actively using them with just the overviews in this book.
There's no chapter on Support Vector Machines, just a section on using libsvm, a library that implements SVM. They said an in-depth discussion of SVM was beyond the scope of the book. I strongly di...more
There's no chapter on Support Vector Machines, just a section on using libsvm, a library that implements SVM. They said an in-depth discussion of SVM was beyond the scope of the book. I strongly di...more
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Has a copy to sell/swap
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Read in February, 2008
This review has been hidden because it contains spoilers. To view it,
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This book is a survey of machine learning algorithms useful for tasks like spam filters and recommendation engines. It's a great book if you're a practicing programmer that want to get thing done, less great if you're looking for a deep exploration of a particular topic.
There's a few things I liked about it. The most important feature of the book is its breadth. It covers a variety of useful algorithms, from more well known techniques (Baysian filters) to recent developments (supp...more
There's a few things I liked about it. The most important feature of the book is its breadth. It covers a variety of useful algorithms, from more well known techniques (Baysian filters) to recent developments (supp...more
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Read in December, 2007
This is one of my first books about application building, as opposed to User Interface or general Computer Science. There's a lot more math than I'm used to -- every example so far contains a mathematical function.
So far I've seen how to calculate movie recommendations from a list of critics' ratings. This is something I never thought about doing before, and it's surprisingly easy -- basically comes down to plotting the different ratings as points on a graph.
One thing ...more
So far I've seen how to calculate movie recommendations from a list of critics' ratings. This is something I never thought about doing before, and it's surprisingly easy -- basically comes down to plotting the different ratings as points on a graph.
One thing ...more
Read in January, 2009
The book is good for learning algorithms for getting recommendations and finding patterns in a set of data, but ignores (on purpose) an important point : techniques for working on a huge amount of data. Techniques such as indexing, server clusters, caching, etc. These are key point for making algorithms in this book really useful in real life applications.
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Read in September, 2008
Very interesting look at the algorithms behind recommendations and trending as they apply to web2.0
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04/07/09
Maoxinsheng
is currently reading it
This is a really helpful book in web2.0
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Read in July, 2008
recommended to Mike by:
Amazon Suggestion (search for AI)recommends it for: programmers, AI people
This book is a great introduction to the algorithms that power things like Google's search engine, Amazon's suggestions, and other web sites that use collective user input to extract useful information. There is a great mix of code, statistics, and AI in this book-- it isn't a light fluffy treatment.
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Read in February, 2009
Excellent primer on the techniques modern sites are using, and users are starting to expect.
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Read in December, 2007
Seriously, wow. If you have ever been intimidated by any informational retrieval technologies, or just wanted a clear explanation of how things work -- this book is fantastic. Anyone who can explain k-means clustering clearly in less than 5 pages is absolutely brilliant.
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Read in October, 2008
One of the best computer programming books that I have ever read. It teaches you concepts, new ways of solving problems. It is very relevant to today's enviroment (web, mashups, etc). Examples are in Python.
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Read in July, 2008
Offers impressive insight into the technologies behind Web 2.0 websites (like this one). Lots of great algorithms and sample implementations. A very usable book, very helpful in several of my current projects.
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Read in November, 2007
Well done. It's A.I. 101, but with working code samples in Python that are as clear as pseudo-code. It was a quick refresher for me that I thoroughly enjoyed. The algorithm appendix is very helpful as well.
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I think it might be a bit complicated for novice readers but overall it's great explaning concepts with very good examples. Cookbook for social web developer.
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01/29/09
Deryck Hodge
marked it as to-read
Had this on my currently reading list, but bumping it to the "to read" list since I have other things in front of this one. (More to come when I read it.)
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08/03/08
fbuser826361981
is currently reading it
I've been working my way through this at a glacial pace. One of these days I'll finish it and my associated, side-project (http://pci4r.rubyforge.org).
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Fascinating: what one can achieve by number crunching. Amazon, Last.fm, ...
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Read in August, 2008
Excellent overview of machine learning techniques for data mining.
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