Poor attempt - Rated 
Whilst the book starts out positively enough (hence the 1 review star), it quickly degenerates into a rambling disorganised text that neither adequately covers collective intelligence, programming nor build smart web 2.0 applications.
Terms and assumptions are used without explanation, important issues are skimmed over lightening speed yet simple issues are repeated ad-nausem.
A better title might be "A light skim over Collective Intelligence for Python Programmers", but anything else is seriously overselling the confusing and frustratingly sparse content.
Brief introduction to machine learning from a practical and non-mathematical perspective - Rated 
This book is essentially a brief introduction to the topic of machine learning from a practical and non-mathematical perspective using code examples written in Python.
The intended audience for this book are developers with no background in machine learning but wanting to use machine learning techniques in their projects. Newcomers to the topic of machine learning may find the book useful but those with a background in maths or machine learning will likely find the book to have a somewhat limited appeal.
great book ! - Rated 
This is a very interesting book. Even though it contains code written in python and I don't know that language, it's clear enough to understand the concepts. It's a great introduction and more to data mining...
Good overview of topic, but assumes you must learn Python - Rated
This book provides an good collection of the various algorithms that can be used in this brave new world of Web 2.0. However, I have found it difficult to use as it has all its code written in Python, a language I know little about. It seems to me that if the algorithms were explained in detail beforehand, rather than having to be worked out from the code, this would be a superb book.
Given the type of analysis this book uses, the language could be either Java (as that pervades everywhere), PHP (the language of server scripting), or (even better) Lisp, which Python attempts to emulate.
It talks about making use of the various APIs that are now available, letting you access data that can be manipulated. But it does not show how the data looks before the programs modify them, so if you want (as I do) to use a different language, you cannot see from what you need to modify, only the result.
Don't get me wrong, this is a good book, but the Python code is poorly laid out (never heard of spaces? And if you have to use spaces to indent, try three spaces), and for those like me who don't read Python it was difficult to see what is going on. I don't want to learn Python just to learn the algorithms. I would, perhaps perversely, be prepared to plough through Lisp code, and it would be good to see the Python code converted to Lisp (and Java for that matter).
So yes, this is an interesting book as it describes areas of interest, given the new types of data that are widely available, but I only give it three stars because it seems to be written for Python users, not the general programming audience.
Keeps coming back, again and again - Rated
This is an excellent book that I keep coming back to again and again. It explains a variety of complex machine learning algorithms with easy to follow, clear, concise code. The only bad word I've heard about this book is that occasionally it would be useful to see the algorithms as equations as well as code, I didn't find that a problem at all though.
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