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Programming Collective Intelligence: Building Smart Web 2.0 Applications

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Programming Collective Intelligence: Building Smart Web 2.0 Applications Cover

 

Synopses & Reviews

Publisher Comments:

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

Book News Annotation:

Underlying the emergence of the field of "collective intelligence," as it is termed by Segaran (a director of software development at Genstruct, a computational biology company), is the huge set of data available on the Internet that can provide potential insights into user experience, marketing, personal tastes, and human behavior in general. He provides guidance on writing Python programs and underlying algorithms for analyzing and understanding data, as well as advice on how to gather data from one's own websites and the websites of others. Chapters cover collaborative filtering, discovering groups through clustering, searching and ranking, optimization, Bayesian document filtering, modeling with decision trees, building price models, advanced classification with kernel methods and support-vector machines, non-negative matrix factorization, and genetic programming. Annotation ©2007 Book News, Inc., Portland, OR (booknews.com)

Synopsis:

This book introduces Web developers to the advanced topic of machine learning and statistics in a clear and concise way, with easy-to-follow examples and code that can be used in their own applications.

Synopsis:

Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data, but how can you find out who's making connections with social media, what theyre talking about, or where theyre located? This concise and practical book shows you how to answer these questions and more. You'll learn how to combine social web data, analysis techniques, and visualization to help you find what you've been looking for in the social haystack, as well as useful information you didn't know existed.

Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools.

  • Get a straightforward synopsis of the social web landscape
  • Use adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, and LinkedIn
  • Learn how to employ easy-to-use Python tools to slice and dice the data you collect
  • Explore social connections in microformats with the XHTML Friends Network
  • Apply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection
  • Build interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits
"Data from the social Web is different: networks and text, not tables and numbers, are the rule, and familiar query languages are replaced with rapidly evolving web service APIs. Let Matthew Russell serve as your guide to working with social data sets old (email, blogs) and new (Twitter, LinkedIn, Facebook). Mining the Social Web is a natural successor to Programming Collective Intelligence: a practical, hands-on approach to hacking on data from the social Web with Python." --Jeff Hammerbacher

Synopsis:

Popular social networks such as Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data. Who's talking to whom? What are they talking about? How often are they talking? Where are they located? This concise and practical book shows you how to answer these types of questions and more. Each chapter presents a soup-to-nuts approach that combines popular social web data, analysis techniques, and visualization to help you find the needles in the social haystack you've been looking for — and some you didn't know were there.

With Mining the Social Web, intermediate-to-advanced Python programmers will learn how to collect and analyze social data in way that lends itself to hacking as well as more industrial-strength analysis. The book is highly readable from cover to cover and tells a coherent story, but you can go straight to chapters of interest if you want to focus on a specific topic.

  • Get a concise and straightforward synopsis of the social web landscape so you know which 20% of the space to spend 80% of your time on
  • Use easily adaptable scripts hosted on GitHub to harvest data from popular social network APIs including Twitter, Facebook, and LinkedIn
  • Learn how to slice and dice social web data with easy-to-use Python tools, and apply more advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection
  • Build interactive visualizations with easily adaptable web technologies built upon HTML5 and JavaScript toolkits

About the Author

Toby Segaran is the author of Programming Collective Intelligence, a very popular O'Reilly title. He was the founder of Incellico, a biotech software company later acquired by Genstruct. He currently holds the title of Data Magnate at Metaweb Technologies and is a frequent speaker at technology conferences.

Table of Contents

Praise for Programming Collective IntelligencePrefaceChapter 1: Introduction to Collective IntelligenceChapter 2: Making RecommendationsChapter 3: Discovering GroupsChapter 4: Searching and RankingChapter 5: OptimizationChapter 6: Document FilteringChapter 7: Modeling with Decision TreesChapter 8: Building Price ModelsChapter 9: Advanced Classification: Kernel Methods and SVMsChapter 10: Finding Independent FeaturesChapter 11: EVOLVING INTELLIGENCEChapter 12: Algorithm SummaryThird-Party LibrariesMathematical FormulasColophon

Product Details

ISBN:
9780596529321
Author:
Segaran, Toby
Publisher:
O'Reilly Media
Manufactured:
O'Reilly Media
Author:
Russell, Matthew A.
Subject:
Programming - General
Subject:
Artificial Intelligence - General
Subject:
Information technology
Subject:
Internet programming
Subject:
Intelligence (AI) & Semantics
Subject:
World Wide Web
Subject:
Software Engineering - Programming and Languages
Subject:
ai;artificial intelligence;collective intelligence;machine learning
Subject:
Artificial Intelligence
Subject:
CourseSmart Subject Description
Edition Description:
Print PDF
Publication Date:
20070831
Binding:
TRADE PAPER
Language:
English
Illustrations:
Y
Pages:
362
Dimensions:
9.19 x 7.00 in

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Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computer Languages » Python
Computers and Internet » Computers Reference » General
Computers and Internet » Internet » Programming
Computers and Internet » Internet » Web Publishing
Computers and Internet » Software Engineering » Algorithms
Computers and Internet » Software Engineering » Programming and Languages

Programming Collective Intelligence: Building Smart Web 2.0 Applications New Trade Paper
0 stars - 0 reviews
$39.99 In Stock
Product details 362 pages O'Reilly Media - English 9780596529321 Reviews:
"Synopsis" by ,
This book introduces Web developers to the advanced topic of machine learning and statistics in a clear and concise way, with easy-to-follow examples and code that can be used in their own applications.
"Synopsis" by ,

Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data, but how can you find out who's making connections with social media, what theyre talking about, or where theyre located? This concise and practical book shows you how to answer these questions and more. You'll learn how to combine social web data, analysis techniques, and visualization to help you find what you've been looking for in the social haystack, as well as useful information you didn't know existed.

Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools.

  • Get a straightforward synopsis of the social web landscape
  • Use adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, and LinkedIn
  • Learn how to employ easy-to-use Python tools to slice and dice the data you collect
  • Explore social connections in microformats with the XHTML Friends Network
  • Apply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection
  • Build interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits
"Data from the social Web is different: networks and text, not tables and numbers, are the rule, and familiar query languages are replaced with rapidly evolving web service APIs. Let Matthew Russell serve as your guide to working with social data sets old (email, blogs) and new (Twitter, LinkedIn, Facebook). Mining the Social Web is a natural successor to Programming Collective Intelligence: a practical, hands-on approach to hacking on data from the social Web with Python." --Jeff Hammerbacher

"Synopsis" by ,

Popular social networks such as Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data. Who's talking to whom? What are they talking about? How often are they talking? Where are they located? This concise and practical book shows you how to answer these types of questions and more. Each chapter presents a soup-to-nuts approach that combines popular social web data, analysis techniques, and visualization to help you find the needles in the social haystack you've been looking for — and some you didn't know were there.

With Mining the Social Web, intermediate-to-advanced Python programmers will learn how to collect and analyze social data in way that lends itself to hacking as well as more industrial-strength analysis. The book is highly readable from cover to cover and tells a coherent story, but you can go straight to chapters of interest if you want to focus on a specific topic.

  • Get a concise and straightforward synopsis of the social web landscape so you know which 20% of the space to spend 80% of your time on
  • Use easily adaptable scripts hosted on GitHub to harvest data from popular social network APIs including Twitter, Facebook, and LinkedIn
  • Learn how to slice and dice social web data with easy-to-use Python tools, and apply more advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection
  • Build interactive visualizations with easily adaptable web technologies built upon HTML5 and JavaScript toolkits

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