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4.3 out of 5 stars20
4.3 out of 5 stars
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on 4 December 2007
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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on 22 October 2008
I was a beginner in both python and general programming when I got this book together with a book on how to use Python in general.

I actually found this book more useful than the general Python book as the use of examples is a much better way of learning (in my opinion). The examples introduced get more and more complex through the book which then forces you to learn about those programs in order to understand what they've done.

The fact that it exposes you to live web based data from the beginning is very useful indeed. My only wish would be that the formatting would be a bit clear. Python seems to thrive on using indentation rather than brackets and it can be difficult to see exactly which level of indentation is being used.

You need a ruler to use it!
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on 13 March 2011
This book covers algorithms that elsewhere are treated as artificial intelligence, but here are dealt with very rapidly and in a programmer's mindset. It's great to see ideas that have been considered "academic", such as neural networks, treated as no more frightening than other algorithms such as sorting. The description of how to use live web site APIs is also a great feature of this book.
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on 29 March 2008
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.
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on 11 May 2009
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.
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on 11 September 2008
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
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on 3 August 2010
I have read quite a few books on AI and this is by far the best. It is incredibly well written and makes what others have made difficult very accessible - no matter what your backgroun is. I learnt more reading this book than I have from reading books on the same topic. It is definitely the book for implementers rather than people interested in theory.

I found the examples (in python) excellent and easy to re-implement in C# (almost word for word with LINQ). The explanations were clear, concise and complete.

This guy has a wealth of experience and it clearly shows in his supurb explanations.

If you are interested in AI/Collective Intelligence/how google/amazon etc do what they do - then READ THIS BOOK!!!
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on 10 February 2012
For AI idiots like myself this was a great and non condescending introduction.

Although Python based I found the algorithms clear enough to translate into Erlang without much issue and if you have any functional exposure this should not be difficult.

I'm math phobic but this book really generated my interest with its clarity and applicative nature to everyday problems.

I took this over the competing CI book as that was heavily Java oriented - why do some people mangle Java to any possible task?

The elegance of Python to AI problems really shone through here!

Great Book and not too long either - but certainly not short on knowledge!
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on 24 July 2011
A few people complained about there beeing too little maths in the book, and I would agree. Instead the author explains or at least tries to explain by example and uses python code which can be easily and interactively programed since it is an in-time scripting interpreted language and this for me worked. In fact the author gives the nicest SVM explanation I have come across, and I found that the book reads like a story, so its an entertaining and enjoyable read.

Has a lot of info and the author clearly knows a lot, a wide scope and even prediction markets are covered. So not that bad at all.
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on 7 September 2014
perfect and exactly what I wanted. My interests are with AI and wireless networks and algorithms and software languages. This is exampled in python but covers much more ground. It discuss how companies were able to use algorithms such as google and Microsoft to target personal and useful subject matter and increase their income. It explains how the users on world wide network leave cobwebs everywhere they go which are picked up by these ghost like applications they have created.
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