Programming Collective Intelligence: Building Smart Web 2.0 Applications

Description

Programming Collective Intelligence: Building Smart Web 2.0 Applications

Publisher: O'Reilly Media, Inc.
Number Of Pages: 360
Publication Date: 2007-08-16
Sales Rank: 598
ISBN / ASIN: 0596529325
EAN: 9780596529321
Binding: Paperback
Manufacturer: O'Reilly Media, Inc.
Studio: O'Reilly Media, Inc.
Average Rating: 5

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

Review:
Understanding the logic behind sites like Amazon and Google...

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.

Review:
"Programming Collective Intelligence" - a winner

Web 2.0 is everywhere. When you log into Amazon or Netflix or del.icio.us you'll see sites that know a lot about you and can recommend choices based on past history. The machinery for accomplishing this is nicely laid out in "Programming Collective Intelligence". The math isn't strenuous, but the implications can be profound.

My preference might have been to see examples written in Java, but I've come to appreciate the author's choice of Python. It's been a good motivator for exposing myself to a new language. The code has the virtue of being succinct. I've seen ports to Scheme and JavaScript on the web.

The book is generating some fair buzz. I recommend it highly.

Review:
The imagination and the software

Since I have my first computer, this machine is highly related with the use of my imagination and this make me happy. With this book I rediscover this relation, so I only thinking about looking for time to transform those mental images in to programs.

Review:
One of the BEST book I've read for last 10 years

I bought lots of books on the field of machine learning, but it was hard to understand when it goes deeper with lots of mathmatics. Even though I understand the concept, I had no idea how to implement it.

After reading this book, all the theories that I've been struggling with became very clear. Toby did a great job to explain these tough topics with proper graphics and easy examples.

This book is one of the best book I've ever read for last 10 years (in several hundreds books).

Review:
The most accessible book on machine learning I've found

I first learned of this book just a few weeks ago, shortly before it was available. I immediately read the sample chapter on the publisher's website and was certain I had to get a hold of a copy.

I was not in the least bit disappointed with what I found. It has been quite a while since I've looked at any Python code (I'm more of a Ruby fan, personally), but the code is easy to follow and it's a simple matter to extract the basic concepts into any language.

I have spent quite a few years now watching the field of machine intelligence from the sidelines, occasionally reading the odd technical write up or wikipedia article, trying to wrap my brain around the basic ideas. The thing is, it's not clear to me that in some regards, it's not that complex. It's just that most of the existing books and articles are written for those immersed in the field. This book is not like that. It explains things in clear language that is easy to follow, using simplified examples and making excellent use of graphics to "show" you how it works.

If you really want to dig in deep, Segaran provides exercises at the end of each chapter and gives you an appendix full of mathematical formulas (the "pure" representation of the algorithms).

Finally, I should mention that the last chapter does what so many other technical books should but don't: it clearly summarizes everything he has shown you. He does this in a straightforward way so that you won't have to go searching through the book, rereading everything again, to put these techniques into practice.

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