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Programming Collective Intelligence: Building Smart Web 2.0 Applications (Paperback)

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Key Phrases: document filtering, beautiful soup, blog dataset, Advanced Classification, Building Price Models, New York (more...)
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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


About the Author

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.

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Toby Segaran
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› Visit Amazon's Toby Segaran Page
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Search Inside This Book:
Inside This Book (learn more)
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
document filtering, beautiful soup, blog dataset, weights matrix multiplied, add this function, searcher class, articles matrix, add this method, select rowid, most similar items, add this code, developer key, inbound links, random programs
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Advanced Classification, Building Price Models, New York, Algorithm Summary, Non-Negative Matrix Factorization, Grid War, Evolving Intelligence, Discovering Groups, Making Recommendations, World Bank, Using Stock Market Data, Buddy Chicago, The Kayak, The Night Listener, Python Imaging Library, Les Omaha, Universal Feed Parser, Understanding Kernel Methods, Zooey Akron, Using Inbound Links, The Fisher Method, Basic Google, Real Flight Searches, None Digg, Walt Miami
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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53 Reviews
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98 of 103 people found the following review helpful:
4.0 out of 5 stars Putting Theory into Practice, December 18, 2007
By Syd Logan (Carlsbad, CA USA) - See all my reviews
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This book is probably best for those of you who have read the theory, but are not quite sure how to turn that theory into something useful. Or for those who simply hunger for a survey of how machine learning can be applied to the web, and need a non-mathematical introduction.

My area of strength happens to be neural networks (my MS thesis topic was in the subject), so I will focus on that. In a few pages of the book, the author describes how the most popular of all neural networks, backpropagation, can be used to map a set of search terms to a URL. One might do this, for example, to try and find the page best matching the search terms. Instead of doing what nearly all other authors will do, prove the math behind the backprop training algorithm, he instead mentions what it does, and goes on to present python code that implements the stated goal.

The upside of the approach is clear -- if you know the theory of neural networks, and are not sure how to apply it (or want to see an example of how it can be applied), then this book is great for that. His example of adaptively training a backprop net using only a subset of the nodes in the network was interesting, and I learned from it. Given all the reading I have done over the years on the subject, that was a bit of a surprise for me.

However, don't take this book as being the "end all, be all" for understanding neural networks and their applications. If you need that, you will want to augment this book with writings that cover some of the other network architectures (SOM, hopfield, etc) that are out there. The same goes for the other topics that it covers.

In the end, this book is a great introduction to what is available for those new to machine learning, and shows better than any other book how it applies to Web 2.0. Major strengths of this book are its broad coverage, and the practicality of its contents. It is a great book for those who are struggling with the theory, and/or those who need to see an example of how the theory can be applied in a concise, practical way.

To the author: I expect this book will get a second edition, as the premise behind the book is such a good one. If that happens, perhaps beef up the equations a bit in the appendix, and cite some references or a bibliography for those readers interested in some more in depth reading about the theory behind all these wonderful techniques. (The lack of a bibliography is why I gave it 4 stars out of 5, I really think that those who are new to the subject would benefit greatly from knowing what sits on your bookshelf.)
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52 of 54 people found the following review helpful:
5.0 out of 5 stars Accessible introduction to complex topics, August 17, 2007
By Leo Dirac (Seattle, WA United States) - See all my reviews
(REAL NAME)   
Segaran has done an excellent job of explaining complex algorithms and mathematical concepts with clear examples and code that is both easy to read and useful. His coding style in Python often reads as clearly as pseudo-code in algorithm books. The examples give real-world grounding to abstract concepts like collaborative filtering and bayesian classification.

My favorite part is how he shows us code (gives it to us!) that goes out into the world, grabs masses of data and does interesting things with it. The use of a hierarchical clustering algorithm to dig into people's intrinsic desires in life as expressed in zebo is worth the price of the book alone. The graph that shows a strong connection between "wife", "kids", and "home" but a different connection between "husband", "children", and "job" is IMHO just fascinating.

Gems like that make this book worth reading cover to cover. After that it can happily hang out on your shelf as a reference anytime you need to build something to mine user data and extract the wisdom of crowds.
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42 of 44 people found the following review helpful:
5.0 out of 5 stars Understanding the logic behind sites like Amazon and Google..., October 20, 2007
By Thomas Duff "Duffbert" (Portland, OR United States) - See all my reviews
(TOP 50 REVIEWER)    (REAL NAME)      
Have you ever wondered how some of those "collective intelligence" sites work? How Amazon can suggest books that you'll like based on your browsing history? How a search engine can rank and filter results? Toby Segaran does a very good job in revealing and teaching those types of algorithms in his book Programming Collective Intelligence: Building Smart Web 2.0 Applications. While I'm not ready to run out and build my own version of Facebook now, at least I can start to understand how sites like that are designed.

Contents:
Introduction to Collective Intelligence; Making Recommendations; Discovering Groups; Searching and Ranking; Optimization; Document Filtering; Modeling with Decision Trees; Building Price Models; Advanced Classification - Kernel Methods and SVMs; Finding Independent Features; Evolving Intelligence; Algorithm Summary; Third-Party Libraries; Mathematical Formulas; Index

In each of the chapters, Segaran takes a type of capability, be it decision-making or filtering, and shows how a programming language can be used to build that feature. His examples are all in Python, so it helps if you are already familiar with that language if you want to actually work with the code. But even if you don't know Python, the examples are clear and detailed enough that you can follow along and get the gist of what's happening. I personally think that it would help immensely if you had a background in mathematics and statistics. You can use the code here without having a detailed understanding of math, but I'm sure much of this would be more deeply appreciated if you already know about such things as Tanimoto similarity scores, Euclidean distances, or Pearson coefficients.

From my perspective (a non-Python programmer *without* the math background), I was more interested in understanding the overall picture about things like how ranking systems work or how recommendation engines are structured. While there was more detail than I needed (or understood), I still felt as if I accomplished my goal. I have a much greater appreciation for what companies like Google and Amazon have done in building web applications that allow the knowledge and wisdom of groups to be gathered and applied to my own preferences.

Statistical programmers will probably find years of entertainment here. :) "Normal" programmers will expand their horizons, too.
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Most Recent Customer Reviews

4.0 out of 5 stars Programming Collective Intelligence
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,... Read more
Published 9 days ago by Eric Jain

5.0 out of 5 stars Programming collective intelligence Book Review
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. Read more
Published 1 month ago by Dinesh K. Murali

4.0 out of 5 stars Good broad and introductory coverage of collective intelligence
In the preface I think that the author minimizes the experience a reader must have to get the most out of this book. Read more
Published 2 months ago by calvinnme

5.0 out of 5 stars Awesome!
Tons of great ideas in this book, presented in a useful manner that builds one topic upon the other where applicable. Read more
Published 2 months ago by Robert Ames

5.0 out of 5 stars A practical introduction
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... Read more
Published 3 months ago by D. Brown

5.0 out of 5 stars Great introductory material
This book gives perhaps the greatest introductory insight into the workings of intelligent algorithmic computation. Read more
Published 4 months ago by Michael Hawkins

5.0 out of 5 stars 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. Read more
Published 5 months ago by Nicholas Sardo

4.0 out of 5 stars 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... Read more
Published 6 months ago by Giuseppe Turitto

4.0 out of 5 stars 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. Read more
Published 6 months ago by Digital Puer

3.0 out of 5 stars 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... Read more
Published 8 months ago by Samuel Moñux

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