Programming Collective Intelligence

Programming Collective Intelligence : Building Smart Web 2.0 Applications

4.07 (1,290 ratings by Goodreads)
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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 a dataset * 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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Product details

  • Paperback | 362 pages
  • 178 x 234 x 26mm | 639.56g
  • Sebastopol, United States
  • English
  • 1, black & white illustrations
  • 0596529325
  • 9780596529321
  • 77,232

About Toby Segaran

Toby Segaran is a software developer and manager at Genstruct, a computational systems biology company. He has written free web applications for his own use and put them online for others to try, including: tasktoy, a task management system; Lazybase, an online application that lets users design, create and share databases of anything they like; and Rosetta Blog, an online tool for practicing Spanish and French by reading blogs along with their translations and lists of common words. Each of these has several hundred regular users. His blog is located at
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Rating details

1,290 ratings
4.07 out of 5 stars
5 37% (472)
4 39% (506)
3 20% (255)
2 4% (46)
1 1% (11)

Our customer reviews

This book is a joy. The author examines a number of machine learning algorithms applied to mine collective intelligence for clustering, classifying, recommendations, searching, optimization etc, and guides you through them smoothly at the algorithmic level with minimal mathematics. I was completely new to the domain and found it very easy to follow the entire content apart from the neural networks section. This should not be taken as a point of criticism, as having more experience in the domain now, I believe that neural networks cannot be possibly explained in a few pages; a whole new book would be required. Other than that, the book will teach easily even an absolute novice. There is a short but helpful introduction to Python as well. On the other hand, the accompanying website which is supposed to include material that would help you implement the ideas in the book was non-existent at the times I attempted to access more
by Aris
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