Segaran - Programming Collective Intelligence
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- Book:Programming Collective Intelligence
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- Year:2008
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Programming Collective Intelligence: summary, description and annotation
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Abstract: 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 youve found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and ex
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Copyright 2008 Toby Segaran
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Supplemental files and examples for this book can be found at http://examples.oreilly.com/9780596529321/. Please use a standard desktop web browser to access these files, as they may not be accessible from all ereader devices.
All code files or examples referenced in the book will be available online. For physical books that ship with an accompanying disc, whenever possible, weve posted all CD/DVD content. Note that while we provide as much of the media content as we are able via free download, we are sometimes limited by licensing restrictions. Please direct any questions or concerns to .
"I review a few books each year, and naturally, I read a fair number during the course of my work. And I have to admit that I have never had quite as much fun reading a preprint of a book as I have in reading this. 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."
"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 used directly to analyze social interaction across the Web today. If I had this book two years ago, it would have saved me precious time going down some fruitless paths."
" Programming Collective Intelligence is a stellar achievement in providing a comprehensive collection of computational methods for relating vast amounts of data. Specifically, it applies these techniques in context of the Internet, finding value in otherwise isolated data islands. If you develop for the Internet, this book is a must-have."
The increasing number of people contributing to the Internet, either deliberately or incidentally, has created a huge set of data that gives us millions of potential insights into user experience, marketing, personal tastes, and human behavior in general. This book provides an introduction to the emerging field of collective intelligence . It covers ways to get hold of interesting datasets from many web sites you've probably heard of, ideas on how to collect data from users of your own applications, and many different ways to analyze and understand the data once you've found it.
This book's goal is to take you beyond simple database-backed applications and teach you how to write smarter programs to take advantage of the information you and others collect every day.
The code examples in this book are written in Python, and familiarity with Python programming will help, but I provide explanations of all the algorithms so that programmers of other languages can follow. The Python code will be particularly easy to follow for those who know high-level languages like Ruby or Perl. This book is not intended as a guide for learning programming, so it's important that you've done enough coding to be familiar with the basic concepts. If you have a good understanding of recursion and some basic functional programming, you'll find the material even easier.
This book does not assume you have any prior knowledge of data analysis, machine learning, or statistics. I've tried to explain mathematical concepts in as simple a manner as possible, but having some knowledge of trigonometry and basic statistics will be help you understand the algorithms.
The code examples in each section are written in a tutorial style, which encourages you to build the applications in stages and get a good appreciation for how the algorithms work. In most cases, after creating a new function or method, you'll use it in an interactive session to understand how it works. The algorithms are mostly simple variants that can be extended in many ways. By working through the examples and testing them interactively, you'll get insights into ways that you might improve them for your own applications.
Although the algorithms are described in words with explanations of the formulae involved, it's much more useful (and probably easier to follow) to have actual code for the algorithms and example problems. All the example code in this book is written in Python, an excellent, high-level language. I chose Python because it is:
Code written in dynamically typed languages such as Python tends to be shorter than code written in other mainstream languages. This means there's less typing for you when working through the examples, but it also means that it's easier to fit the algorithm in your head and really understand what it's doing.
Easy to readPython has at times been referred to as "executable pseudocode." While this is clearly an exaggeration, it makes the point that most experienced programmers can read Python code and understand what it is supposed to do. Some of the less obvious constructs in Python are explained in the "" section below.
Easily extensiblePython comes standard with many libraries, including those for mathematical functions, XML (Extensible Markup Language) parsing, and downloading web pages. The nonstandard libraries used in the book, such as the RSS (Really Simple Syndication) parser and the SQLite interface, are free and easy to download, install, and use.
InteractiveWhen working through an example, it's useful to try out the functions as you write them without writing another program just for testing. Python can run programs directly from the command line, and it also has an interactive prompt that lets you type in function calls, create objects, and test packages interactively.
MultiparadigmPython supports object-oriented, procedural, and functional styles of programming. Machine-learning algorithms vary greatly, and the clearest way to implement one may use a different paradigm than another. Sometimes it's useful to pass around functions as parameters and other times to capture state in an object. Python supports both approaches.
Multiplatform and freePython has a single reference implementation for all the major platforms and is free for all of them. The code described in this book will work on Windows, Linux, and Macintosh.
For beginners interested in learning about programming in Python, I recommend reading Learning Python by Mark Lutz and David Ascher (O'Reilly), which gives an excellent overview. Programmers of other languages should find the Python code relatively easy to follow, although be aware that throughout this book I use some of Python's idiosyncratic syntax because it lets me more directly express the algorithm or fundamental concepts. Here's a quick overview for those of you who aren't Python programmers:
Python has a good set of primitive types and two that are used heavily throughout this book are
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