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Message from Dean - May 8th 2007
I am currently testing out a new version of the APF Bridge Component - If you notice any errors within this demo store please drop me a line.
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One of the best books I have bought in a while. It strikes a perfect balance of introduction of the algorithms and practical application. The book is organized around the different problem areas such as "search", "optimization", "categorizing", etc. and algorithms to achieve them. It starts each section with a naive implementation to a problem, and gradually works through to more intelligent solutions. I really enjoyed the evolution of the search implementation. It starts with a trivial implementation, and continues to augment adding such features as a simplified PageRank and other optimizations.
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I've had this book for almost a year now, and have been pleased with the content.
If you run a website, with users, you probably have a ton of data on how those users use your site. Each chapter covers a different topic in things like search ranking, recommendations (they kind of explain how amazon does it!), plus a ton of other statistical methods and models to help improve your data analysis. The code examples are fairly straight-forward, written in python, and easy to adapt to other programming languages with minimal effort.
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I've actually bought this book for my bf who is one of those self taught people and he was jumping off the walls when he got it. He has been smiling for days....
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This tutorial does much more than it promises. It provides a thorough introduction to Web 2.0 techniques for preference and recommendation applications and many search related algorithms, based upon statistical learning techniques and showing use of the relevant web application's API (software interface). At the same time it offers an excellent tutorial in one of computer science's most widely relevant topics: Machine Learning. This mathematically sophisticated topic is carefully explained to the layman in both a verbal description of how each algorithm works, and well documented Python code to allow both deep understanding and practical experience with relevant programming examples. I would highly recommend this book to anyone who wants to understand important Web 2.0 concepts involving analysis of information and data or who needs to implement a Machine Learning or Data Mining application. The only recommendation I would make to the author for a second excellent edition would be the inclusion of an appendix on some popular Machine Learning package such as Weka, or for Python compatibility Orange. All in all a valuable addition to any web programmer's library and as a guide to Statistical Learning concepts.
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I was surprised not to see a 'ONE STAR' Rating I guess most readers are above average.
The biggest problem I found was use of Python script. I don't know Python and this books uses it heavily to an extent that I had to pick a Python book.
Limited theory for example neural network it has been almost 7-8 years I read about them. This gives only a brief introduction nothing more.
Is this book for you
1. If you are expert in Python and have worked on web technologies (Search Engines etc).
2. If you have sound theory background know Clustering Algorithms and neural network.
What this book teaches you:
Simply put it helps manipulating internet data in various ways. These algorithms are heavily used by companies like Amazon, Ebay, Google and Yahoo to provide cool applications to user.
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