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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

4.06 of 5 stars 4.06  ·  rating details  ·  762 ratings  ·  65 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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Pattern Recognition and Machine Learning by Christopher M. BishopThe Elements of Statistical Learning by Trevor HastieProgramming Collective Intelligence by Toby SegaranAll of Statistics by Larry WassermanArtificial Intelligence by Stuart Russell
Machine Learning
3rd out of 23 books — 13 voters
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Best Python programming books
9th out of 9 books — 8 voters

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Community Reviews

(showing 1-30 of 2,034)
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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 disagree; They
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the code references have since been updated, and in some cases completely rewritten, so the code samples are sometimes out of date in non-obvious ways.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 bef
Wael Al-alwani
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
Michał Szajbe
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
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
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 to Chapter 12
Matthew Witmer
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 of topics rel
Wai Yip Tung
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
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 vector mach
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
Segaran does an impressive job in this book of rendering in code and English most of the confusing math that has led to the current state of the art in classification, clustering, collaborative filtering, text indexing, and even neural networks ! This book is really heavy on the example code, which is fantastic for engineers, and extremely light on math, which I think is largely a plus. (The approach breaks down in the section on SVMs, since understanding these monsters without math is, I think, ...more
Noah Sussman
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 ratings as po
Skanda Vasudevan
A very concise book on some quick hands on experience to build some intelligent systems. Search engines, Recommender Systems etc. are explained in a preliminary way so any one who wants to get a first hand on developing these kind of systems can go for this
Amar Pai
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 hack around, this is a great book to sit down with.
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:
Crawling, indexing, ranking (frequency, distance,
Will Johnson
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 / method was introduced without much
Read this a long time ago. Really enjoyed it then.
Adron Hall
Overall, I liked the book. I would have loved to actually implement more of the things discussed in the book, but the topic is a bit abstract and it takes time to lay down real life examples. Even amidst my lack of creating samples and working with real live code on the matter at the time, the book reads pretty good and the content is good. It's by no means a super advanced book on the topic, but will definitely give you a good basis and fill in any gaps in thinking through problems in the colle ...more
Agustin Colchado
Superficially covered the topic. Needed a bit more real world topics.
Jarrod Parker
Great book for those who are new to Machine Learning.
Provides very well written descriptions and walk throughs of some of the most common algorithms used in Machine Learning.
-recommendation engines
-search engines
-mathematical optimization
-finding relationships in text
-decision trees
-price models
-support vector machines
-feature finding
-genetic programming
and more

Includes a lot of code examples.
I would have given 5 stars, however there were a a few code errors in the examples.
Sep 23, 2014 Luiza rated it 3 of 5 stars
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.
Christian Brumm
Good, very practical first read on machine learning applications. Solves real problems using real python code. Covers some intuition about the algorithms, but will do only little in terms of real understanding. I found the complete lack of formulas (and instead always python code) to be disturbing - sometimes a succinct mathematical notation is just way easier to understand - but I guess that depends on your taste and background.
Michael Ryan
A fantastic book for geeks who want to run wild with Bayesian filtering, machine learning and big data in Python.

Really practical and really interesting.

The last time I picked up a programming book and thought, 'Wow, I've really got something here,' would have been Nicklaus Wirth's Algorithms + Data Structures = Programs, back in the late 1970's. This book may well become a classic of the genre for similar reasons.
Ariovaldo Junior
its a great book.
Derek Bridge
I've come to this book quite late - it was published in 2007. Some of it is now showing its vintage: it uses Python 2, but Python 3 was just released; and some of the APIs it used have changed.

Nevertheless, it's still a worthwhile read. It covers remarkable ground, ripping through a swathe of data mining techniques, all illustrated with code in Python, and all with social media applications. Cool.
Kyle Pace
Material was helpful and presented in an easy to understand manner.

Would've liked more discussion on theory for the algorithms instead of code. I would recommend anyone with an interest in the subject start here.

Note: A large portion of (re: almost all) the links to the author's page are broken and some of the API's used in examples are out of date.
Ryan  O'Neill
This book is so excellent that even though almost every single coding example in the book is broken in some way, I am still giving it five stars. Many of the real-world examples no longer work due to the book being 5 years old a this point. However, the information within the book is all still relevant and becoming increasingly important in today's technical landscape. Highly Highly Recommended.
Este libro es un compendio de técnicas de aprendizaje automático y muestra su aplicación en conjuntos colectivos de datos, como los que se encuentran actualmente disponibles a través de redes sociales u otras aplicaciones web.

Cada técnica es introducida, pero sin entrar en detalles de su uso.

Es un material que podría ser conplementario a algún curso de IA.
The book is good for learning algorithms for getting recommendations and finding patterns in a set of data, but ignores (on purpose) an important point : techniques for working on a huge amount of data. Techniques such as indexing, server clusters, caching, etc. These are key point for making algorithms in this book really useful in real life applications.
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