Programming Collective Intelligence: Building Smart Web 2.0 Applications

Product Description:
Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general--all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:

• Collaborative filtering techniques that enable online retailers to recommend products or media
  
• Methods of clustering to detect groups of similar items in a large dataset
  
• Search engine features--crawlers, indexers, query engines, and the PageRank algorithm
  
• Optimization algorithms that search millions of possible solutions to a problem and choose the best one
  
• Bayesian filtering, used in spam filters for classifying documents based on word types and other features
  
• Using decision trees not only to make predictions, but to model the way decisions are made
  
• Predicting numerical values rather than classifications to build price models
  
• Support vector machines to match people in online dating sites
• Non-negative matrix factorization to find the independent features in adataset
  
• Evolving intelligence for problem solving--how a computer develops its skill by improving its own code the more it plays a game

Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you.

"Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."
-- Dan Russell, Google

"Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."
-- Tim Wolters, CTO, Collective Intellect

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Customer Reviews

Rating: - Great introductory material
This book gives perhaps the greatest introductory insight into the workings of intelligent algorithmic computation.It covers everything from page rank to neural networks and so much more.Its easy enough to understand, even for a non-math major, and the python code samples are concise, accurate and functional.

Would highly recommend this book for application and web developers who are creating or just interested in intelligent, data driven utilities.
Rating: - A very interesting book
I picked this book up at a local Barnes and Noble.While I am certainly not trained in some of the areas this book covered, I found them completely accessible.While it should be obvious from the title that someone new to programming would find this book an incredibly tough read, I'll state it for the record.If you are learning how to program, this book is worth purchasing and holding on to until your ready.

The whole idea of "Collective Intelligence" is an interesting one.Given ... Read More
Rating: - Excellent to refresh my knowledge
Back in school, few years ago (to many to remember). I had to study most of this concepts, and at the time they where to abstract to me, and the examples and exercises they where so simple that they weren't making sense in real life. After that I started to work in other kind of system's and projects that never had the chance to play around this concepts and see how to apply them in real life. Until now that I had the chance to read this book, and see how I can apply this ideas and concepts in real ... Read More
Rating: - Great breadth; poor references; crippled by terse Python
This book provides very good breadth on a number of subjects related to machine learning. The author covers unsupervised classification and prediction systems (e.g. Bayesian classification, neural networks, and support vector machines), supervised clustering (e.g. K-Means), and stochastic optimisation (e.g. simulated annealing, genetic algorithms, and genetic programming).

Although I already had some knowledge of genetic algorithms, I know next to little about machine learning in general ... Read More
Rating: - Practical and accessible.
The book is interesting and easy to read. Shows how to apply AI concepts to the kind of applications that the majority of programmers produce, and for those who like me studied AI years ago but haven't used it a lot since then, it's a good reminder.

But, the quality of the Python code leaves a lot be desired. I'm sure it works, and for strict personal use it could be OK, but lacks of ellegance for a textbook; abuses of list comprehensions and long expressions(to make the code compact, I guess), ... Read More