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 Programming Collective Intelligence Nov 11, 2009
It's great to have an accessible algorithms book that doesn't just cover sorting algorithms. This book shows how to implement basic algorithms for clustering, optimization, decision trees etc. For the most part the algorithms are developed from scratch, unlike e.g. in Manning's "Collective Intelligence in Action", which is more of a tutorial on using existing data mining libraries. The drawback is that while the book does a good job of teaching the basics, the algorithms and implementations may be too simplistic for use in a real project.
Python is a good language for code examples, but the code in this book is often a bit too terse (e.g. single letter variables or lists of unnamed parameters), and there are quite a few mistakes in the code.
The "collective intelligence" title and "web 2.0" connection are a bit tenuous. There are some examples where a Web API is used as a source of data, and no doubt some of the algorithms in this book can be useful in such applications. But this is clearly a book about general-purpose algorithms, and not a book about building Web 2.0 sites.
 Programming collective intelligence Book Review Oct 18, 2009
I was recommended to buy this one by my instructor. I wanted a book which described the approaches for building applications in web 2.0. This book exactly served my purpose. The machine learning approach was fantastic. The book was delivered on time without any issues. I received it in perfect condition.
Good broad and introductory coverage of collective intelligence Sep 19, 2009
In the preface I think that the author minimizes the experience a reader must have to get the most out of this book. First off, I think you should be familiar with the general principles of artificial intelligence as covered in Artificial Intelligence: A Modern Approach (2nd Edition), and I think you should also be familiar with the theory of algorithms as covered in Introduction to Algorithms, Third Edition. These are both largely language agnostic books, and I think these types of books do the best job at teaching computer science theory. Finally, the author minimizes the experience you should already have with Python. As the author states, Python reads almost like pseudocode, with "almost" being the operative word here. Just using plain pseudocode or a language that most are familiar with such as C would have been better. The author does not give you enough background on Python that you can pick this book up cold and not be confused. For the task of learning Python the right way I recommend "Learning Python", which is coming out in a brand new edition next month.
On the bright side, though, this is a great introduction to recommender systems and the algorithms used in the collection and analysis of web data. The author clearly states the principles and uses of each algorithm and puts in bits of code as he goes. The illustrations are also excellent. The problem with most of the books on collective intelligence is that they are either doctoral theses - or should be - or they are very elementary books written for people using software packages that do the analysis for them, thus exposing few details. This book strikes a great balance and hits the target for the professional who needs to learn this material quickly.
The exercises are pretty good and are a combination of programming assignments and "do you think X is possible?" types of questions. Of course, what I think is possible doesn't matter, the question is answered if I am able to implement a solution or at least sketch one out. There are no answers to exercises here, so you'll never know if you are right unless you do implement a solution that answers the question.
All in all, I recommend this text for the qualified reader - a programmer already skilled in Python and knowedgeable in artificial intelligence and efficient algorithm implementation - in other words, the working professional.
Awesome! Sep 13, 2009
Tons of great ideas in this book, presented in a useful manner that builds one topic upon the other where applicable. Very easy to understand, looks like it's very easy to apply as well.
A practical introduction Aug 21, 2009
This book helped me get real machine learning concepts into my code quickly. It does a good job covering a broad range of techniques, and the examples are interesting and useful. I appreciated the illustrations as a visual way to explain the concepts in the text.
On the negative side, the book's emphasis on readability prevents it from going into deep detail on the subjects covered. In particular, I would have like to see more discussion about feature selection with many examples showing good and bad choices for features used in different models. This is the flip-side of the book's strength, namely that it is very readable. Another way this comes up is that the code in the book may not be very efficient of fast. Fortunately, the author's code is written in a way to make it easy to improve. Also it's worth noting that even simple ML algorithms can be quite powerful when used with enough data, eg. Bayesian spam filtering.
Bottom line: As someone starting to write smart applications, this book was definitely worth the cost in money and time.
For more detailed coverage of machine learning, I can recommend these two books (perhaps to be read after Collective Intelligence):
Russel and Norvig's Artificial Intelligence, A Modern Approach
Hastie and Tibshirani and Friedman's The Elements of Statistical Learning
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