Customer Reviews:
 very good introduction September 11, 2008
Peter Martin Hartwig (Copenhagen, denmark)
This book really does an awesome job at guiding you through the process of using collective intelligence in your own applications. For me, a bit more explanation on the math would be nice, but if i just pay attention it still seems to stick, so it's not crucial.
just, well, get it! it's good
Pete
A truly outstanding book March 29, 2008
sprocketonline (London)
5 out of 5 found this review helpful
This book is definitely worth having on your bookshelf. It breaks down one of the most complex and demanding subjects into delightful, succinct and digestible pieces.
Having dredged my way through similar books in the past, I wasn't expecting too much, but Programming Collective Intelligence is remarkable. I found this book gripping; completing each example was very rewarding, and has now got me hooked on learning more.
I ended up working my way through the entire book, and really feel that it was time well spent. I now feel that I actually understand the concepts and algorithms surrounding machine learning/AI/data mining, and as a bonus have become familiar with a huge number of web 2.0 APIs.
The source code is almost impeccable. Most programming books falter on sloppy and/or incomplete code, but Programming Collective Intelligence is well explained and has the complete Python code written in the book, as well as being available for download. I was able to do every example without any major problems, despite having never used the Python language before.
Well written & diagrammed, with good examples and wonderful explanations: this a fantastic book, and to be highly recommended.
excellent - accessible machine learning December 4, 2007
B. OSullivan
18 out of 18 found this review helpful
This is excellent - forget the marketing rubbish about making better web 2.0 apps: It is all about machine learning - the science of how Amazon and others can make recommendations based on the ordering patterns of others.
The difference between it and other texts on this machine learning, is how accessible it is, and how apt the data sets it chooses are. Machine learning is an active area of research, and I was surprised that this book even covers kernel methods.
It generates a real appetite to learn more about the theory of machine learning: Which you will need, as most machine learning text books are mathematically tough going and dry.
I only wish I could skip the day job for a week and study this book from cover to cover.
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