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Amazing piece of work. Makes much sense of the power of software and algorithms in predicting customer preferences and behavior based on aggregate data. Very well done with concise examples in Python programming language. Entire book is quite focused on modern web sites and web technology. Read full review
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One of my favorite AI book ever. This book is simple to understand and provides easy examples in Python.
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Programming Collective Intelligence is a new book from O'Reilly, which was written by Toby Segaran. The author graduated from MIT and is currently working at Metaweb Technologies. He develops ways to put large public datasets into Freebase, a free online semantic database. You can find more information about him on his blog: http://blog.kiwitobes.com/.
Web 2.0 cannot exist without Collective Intelligence. The "giants" use it everywhere, YouTube recommends similar movies, Last.fm knows what would you like to listen and Flickr which photos are your favorites etc. This technology empowers intelligent search, clustering, building price models and ranking on the web. I cannot imagine modern service without data analysis. That is the reason why it is worth to start read about it.
There are many titles about collective intelligence but recently I have read two, this one and "Collective Intelligence in Action". Both are very pragmatic, but the O'Railly's one is more focused on the merit of the CI. The code listings are much shorter (but examples are written in Python, so that was easy). In general these books comparison is like Java vs. Python. If you would like to build recommendation engine "in Action"/Java way, you would have to read a whole book, attach extra jar-s and design dozens of classes. The rapid Python way requires reading only 15 pages and voila, you have got the first recommendations. It is awesome!
So how about the rest of the book, there are still 319 pages! Further chapters say about: discovering groups, searching, ranking, optimization, document filtering, decision trees, price models or genetic algorithms. The book explains how to implement Simulated Annealing, k-Nearest Neighbors, Bayesian Classifier and many more. Take a look at the table of contents (here: http://oreilly.com/catalog/9780596529321/preview.html), it does not list all the algorithms but you can find more information there.
Each chapter has about 20-30 pages. You do not have to read them all, you can choose the most important and still know what is going on. Every chapter contains minimum amount of theoretical introduction, for total beginners it might be not enough. I recommend this book for students who had statistics course (not only IT or computing science), it will show you how to use your knowledge in practice – there are many inspiring examples.
For those who do not know Python - do not be afraid – at the beginning you will find introduction to language syntax. All listings are very short and well described by the author – sometimes line by line. The book also contains necessary information about basic standard libraries responsible for xml processing or web pages downloading.
If you would like to start to learn about collective intelligence I would strongly recommend reading “Programming Collective Intelligence” first, then “Collective Intelligence in Action”. The first one shows how easy it is to implement basic algorithms, the second one would show you how to use existing open source projects related to machine learning.
You can find more about this book on it's catalogue page: http://oreilly.com/catalog/9780596529321/