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Programming Collective Intelligence: Building Smart Web 2.0 Applications

4.07  ·  Rating details ·  1,402 ratings  ·  91 reviews
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 ot ...more
Paperback, 362 pages
Published August 23rd 2007 by O'Reilly Media (first published 2002)
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Nov 06, 2007 rated it liked it
This is a beginner's guide to machine learning techniques. In typical O'Reilly fashion, there's very little math but lots of code snippets. While you will learn some motivation for using various techniques, you won't be able to start actively using them with just the overviews in this book.

There's no chapter on Support Vector Machines, just a section on using libsvm, a library that implements SVM. They said an in-depth discussion of SVM was beyond the scope of the book. I strongly di
Nov 23, 2012 rated it liked it
5 years ago, this may have been *the* book for the aspiring Artificial Intelligence practioner. It hasn't held up as well, or maybe I'm just a lazy whiner, but this book requires far more effort than normal. The libraries the code references have since been updated, and in some cases completely rewritten, so the code samples are sometimes out of date in non-obvious ways. The confirmed and uncomfirmed errata must be kept open in your browser at all time.

Chapter 12 (the summary) should be read before reading the r
Feb 17, 2008 rated it liked it
This review has been hidden because it contains spoilers. To view it, click here.
Wael Al-alwani
Nov 10, 2011 rated it it was amazing
This book was extremely helpful in refreshing my knowledge in many topics I came across in the fields of machine learning, data mining, and optimization. 5 stars to this book for being easy to read and well written, presenting some really sophisticated concepts in a very neat way, and finally putting all these concepts along with interesting ideas and examples all in one place. Some said that many explained techniques are not very useful anymore with the excessive loads of data the nowadays-appl ...more
Will Johnson
May 05, 2012 rated it liked it
Shelves: machine-learning
I'm not exactly who this book was for.

The problem was twofold.

1.) There were a lot of errors in the book. O'reilly's unofficial errata is filled with examples of where the code is incorrect or output in the book doesn't match the actual output you should receive.

2.) The statistical concepts are kind of brushed over. If this book is for programmers wanting to learn about collective intelligence, then it did a poor job in conveying the algorithms. An algorithm /
Dec 27, 2013 rated it liked it
Shelves: 2013
Good practical guide for a first contact with analytics, but does not go too deep on the explanations. It's much better for coding examples and to see results quickly, but most of the times you feel there's something missing on the explanations. The book also needs a good revision, since some of the APIs described are not available or had changes in the last years. The links provided by the book are also broken, would be much better if the author had used tools like for the URLs.
Sep 13, 2014 rated it liked it
Shelves: tech
Too much focus on data scraping at the expense of algorithmic/mathematical theory.
Alex Ott
May 06, 2010 rated it really liked it
Very good introduction into machine-learning, information retrieval & data mining related questions. Could be used to get high-order overview of corresponding topics, especially by non-CS peoples.
Costin Manda
Feb 27, 2019 rated it it was amazing
Shelves: owned
Programming Collective Intelligence is easy to read, small but concise, and its only major flaw is the title; and that is because it is misleading. The book touches quite heavily on using collective information and social site APIs, but what it is really about is data mining. It may not be a flaw with the majority of readers, but personally I wouldn't care about the collective, the Facebook API or anything like that, but I was really interested in the different ways to analyse data. In that sens ...more
Matthew Witmer
Oct 15, 2010 rated it really liked it
Programming Collective Intelligence (Segaran, 2007) uses a multitude of examples to show how data can be combined and analyzed to produce results that are “more human.” The book intersperses text with Python programming snippets. The programming code allows someone to work through all of the examples discussed in the book. At times, some more advanced examples require additional library downloads, but everything in the book is accessible to the reader.

The book covers a wide range o
Alex Smirnov
I've started getting acquainted with machine learning with this book. It covers basic ideas from the ground up and doesn't rely on knowledge of statistics and deeper math. The author is not using Python ML ecosystem and builds all algorithms from the start - which is pretty good if you want to understand the internals of the algorithms. The book covers recommendation systems, classifiers, clustering, and regression models as well as less obvious searching and ranking, optimization and genetic pr ...more
Oliver Day
May 22, 2017 rated it really liked it
great intro to ML/AI algorithms, having worked through the code I can tell you it's worth it, but have the errata page handy on O'Reilly's website as there are often slight mistakes or tweaks. My favourite was chapter 11 genetic programming and chapter 4 for web search engines, bit outdated in places
Mridul Singhai
Aug 21, 2018 rated it really liked it
Slightly outdated for today's times, but still does a good job at describing the practical techniques required to make small features for a pet web application without all the morbidity that surrounds today's age of statistical inference.
Gregory Reshetniak
May 07, 2019 rated it it was amazing
Amazing read, very captivating. Nice learning curve, no sudden unexplained jumps.
Yang Zhang
May 04, 2017 rated it it was amazing
A perfect book for beginners.
Jul 01, 2017 rated it it was amazing
It's a good book, though it has some little mistakes.
I'll read it again to fully understand it.
Scott Sill
Mar 16, 2017 rated it it was amazing
If you're looking for a great starting point to learning about machine learning & data analytics, this is it. Toby Segaran does an excellent job of explaining the concepts behind collective intelligence then walks your thru the process of writing code to capture/analyze big data sets. I've read this book multiple times and still refer back to it. Highly recommended.
Wai Yip Tung
Dec 21, 2012 rated it really liked it
When this book fist come out in 2007, it generates quite a thrill. For many programmers like me, this opens a door to the world of machine learning. The book introduces a range of machine learn algorithm solving problems such as classification, clustering and optimization by learning from data and making statistical inference. There is little theory or mathematics used. Instead the emphasis is on program code. It does come up with simple but practical data set so that the algorithm makes intuiti ...more
Feb 09, 2014 rated it really liked it
Programming Collective Intelligence: Building Smart Web 2.0 applications was great! I never had a machine learning or AI class in college so this was my first exposure to it. The book gives you an overview of multiple machine learning techniques and then dedicates a chapter to each one. In each section they discuss the mathematical background of the technique and give sample web applications that use it (such as Recommendations or finding dating partners). They also point out scenarios where you ...more
Guilherme d'
The book is quite good to get a general idea how some common algorithms work, and does that in a very nice way. The biggest problem for me, which is not a fault in the book, is that most os the materials that the book uses to teach the algorithms are not available anymore or are outdated, so in the end I end up reading the book only, without applying the code. Maybe the newest edition doesn't have this problem.
Nov 28, 2007 rated it really liked it
This book is a survey of machine learning algorithms useful for tasks like spam filters and recommendation engines. It's a great book if you're a practicing programmer that want to get thing done, less great if you're looking for a deep exploration of a particular topic.

There's a few things I liked about it. The most important feature of the book is its breadth. It covers a variety of useful algorithms, from more well known techniques (Baysian filters) to recent developments (support
Oct 06, 2009 rated it it was amazing
This is an incredibly useful book for all those who are looking to divine intelligence with data collected through their web apps. Segaran mixes equal parts math, theory and practice in a way that keeps the reader's attention while introducing a number of somewhat complex machine learning topics. Python was a wise choice for the example programs as well.

This book does not need to be read in order. In fact, my humble recommendation is to read the introduction in Chapter 1, then skip t
Budhi Wibowo
Sep 12, 2016 rated it it was amazing
Excelent book for introduction to machine learning! Very practical.
Noah Sussman
Jan 01, 2008 rated it liked it
I lost my copy of this book, which is too bad. This book was one of my first books about application building, as opposed to User Interface or general Computer Science. There's a lot more math than I'm used to -- every example so far contains a mathematical function.

So far I've seen how to calculate movie recommendations from a list of critics' ratings. This is something I never thought about doing before, and it's surprisingly easy -- basically comes down to plotting the different r
Jun 18, 2012 rated it liked it  ·  review of another edition
This book does a good job making an introduction of machine learning technologies to the average programmer. This is its main merit. Having said that, the introduction to the subjects is very simplified, so you'll need further reference to actually implement anything at all. It's full of Python code snippets only work to make the subject appear accessible to the programmer, and look like waffle to me. Mathematical formulas in which code snippets are based can only be found (without further expla ...more
Aug 13, 2009 rated it it was ok
Extremely basic if you're familiar at all with ML, but its intended audience probably isn't. Also, the name of the book kinda sucks -- makes me think of something else (not sure what).

Making Recommendations:
He basically says to use Pearson correlation on item/user vectors for item-item/user-user-simalirity...

Discovering Groups:
Hierarchical clustering, k-means clustering, and multidimensional scaling. Again using correlation.

Searching and Ranking: ...more
Nov 07, 2014 rated it it was amazing
Quite a good book with lots of useful code examples in Python. I love the fact that these examples are real world and quite useful and interesting to look at (though not all of them are working as is now, things evolve quickly and so are APIs after all). Also basic artificial intelligence and machine learning techniques are covered (knn, neural nets, svms, decision trees, bayes rule, linear regression, clustering) and even some optimization techniques and a bit of genetic programming.

Amar Pai
Sep 10, 2009 rated it liked it
This is a good overview of various algorithms/techniques used by Google, Netflix and others to do things like

- determine people whose taste in movies is most similar to your own
- given a document, guess which category it belongs in
- figure out what bands you might be interested in

Basically machine learning, categorization, inference, etc.

The examples are all in Python & they are clearly written & easy to follow.

If you have some time to hac
Jun 21, 2016 rated it really liked it
Very entertaining book with very clear examples on introductory machine learning. I would advise a begginer-intermediate level of Python programming, as most of the book is made to be understood by code (the explanations by 'text' are easily understood, however the lack of programming knowledge severely cuts the book's utility).

It is recommended to do as you learn, but most of the book is understandable without actually writing the code yourself - some parts, however, such as the Non
Michał Szajbe
Jul 27, 2014 rated it it was ok
The book seems to be taking practical approach to the subject, but in fact contains code examples that are written poorly to such extent that reader must put too much effort in understanding the code, instead of focusing on the concepts themselves. Take out the code examples and descriptions of some python libs and the books shrinks by 80%.

Experienced python programmers would probably find it easier to comprehend than I did, but it does not change the fact that the code would not really be usab
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