most people have shared their thoughts on the good of this book. I like to point out some of the bad as I read through:

- first, too many typos - both the author and oreilly should do a better job on proof read the materials. the typos are so much that it can easily wreck otherwise good materials.

- second, arcane solution and coding style. Many first step to the solution of machine learning is to represent the problem at hand well. The author's brain apparently wired different from mine so the opinion is personal. For example: chapter 5 on "optimization for preference", he chose to represent a solution as vector form like [0,0,0,0,0,0,0,0,0,0], there is no way I can relate this solution to the real meaning (you want to allocate 10 students into 5 rooms each with two slots) - if there is an easy explanation, the book didn't say so.

thus the 3 star. I believe a second edition is warranted and should be much better.
just my 2c.

Programming Collective Intelligence is a book about applying data mining techniques to analyse collections of data. There is submerged information in Ebay prices, in Facebook profile networks, in collections of movie reviews, in news sites, in the stockmarket; this book by Toby Segaran shows ways to extract, visualise, understand, and predict that information.

Each chapter explains and explores a different data mining algorithm, and builds up a working example in Python, while presenting different methods and parameters of the implementation. I hadn't really worked with Python before, but found the code easy to follow, and picked up some interesting Python idioms that I haven't seen in other languages before. Chapters end with a set of exercises to follow that build your understanding.

As you follow the examples you build up a reasonably generic code base that allows you to swap in and out different implementations, and reuse previous code to add to new applications.

The examples use live examples from the web: sites like Ebay, Facebook, and Yahoo Finance, and this makes the book more interesting and the results more visceral than some other books on the subject which use more contrived or obscure examples. Even though there is a strong web (or web 2.0) focus on the examples, the methods and the understanding is useful for a whole range of applications.

Some of the topics covered:

* Bayesian classifiers to detect spam, or to file news articles into site sections
* Hierarchical and k-means clustering to discover groups of similar items in massive sets
* Euclidiean distance, Pearson Correlation Coefficient, Tanimoto Coefficient: ways to measure the distance (or difference) between items
* Neural networks to predict user behaviour and improve search result ordering
* Optimisation methods like hill climbing, simulated annealing, and genetic algorithms
* Non-negative matrix factorization
* Support vector machines and kernel methods to go where linear regression can't

I found it exciting to read -- it's one of those books that give you a whole bunch of new ideas for things to build as you read it. The presentation is very good: no background is assumed, and it doesn't talk down to those more experienced.

Recommended.

Once I got past the initial shock of finding several glaring grammar and spelling errors in the introduction, I have been pleased with this purchase ever since.

The author gives a good overview the many different approaches to machine language (with great examples in Python). However, it's just that - an overview. While the explanations are very clear and the concepts are presented in a very accessible manner, I found myself having to look elsewhere for more detail on the various algorithms. Yes, with the level of understanding presented in this book you should be able to create functional code for your particular data set. However, I felt that to really get the best results from the algorithms I needed to study them a bit further in order to best apply them to my data.

As a recent CS graduate, I would certainly recommend this book to anyone looking for a basic understanding of machine learning and data mining techniques.

I have just about finished reading this book, and I'm really enjoying it. It's loaded with great information and examples. I like how the author gives the reader tips on when certain techniques are better than others. The python examples are clear and easy to read. I'd love to see more books follow this one's style and structure.

Toby is a genius! He demystifies "Collective Intellignece" and provides some great examples using the public domain Python language. This is one of the best technical books I've ever read as it makes the sometimes difficult connection between theory and practicality.

This is an excellent place to begin for those who have identified the need for a collective intelligence application but are not sure upon which path to take their first step.

Toby's numerous examples are insightful and amusing - and free! It's now required reading for my growing staff of coders. Looking forward to Toby's follow-up (hint, hint).

This is one of those books I wish I had more time to devote to. I've barely begun to read it and already, I'm thrilled with the information being shared - I never knew what I didn't know, but this book has really opened my eyes to an entire facet of my development expertise that needs to improve.

Highly recommended

I think this is a good, easy-to-read intro to several interesting data-centric software technologies, but it is superficial.

For example, their collaborative filtering (ratings + recommendations) section illustrates only the most simplest of algorithms and completely skips over more advanced techniques (improved normalization, matrix factorization, and others), it skips over even basic benchmarking of the rec system (IMO, if you aren't doing objective benchmarks and tuning it off of those metrics, your rec system is useless), and doesn't address any of the common pitfalls and problems (sparsity, overfitting, normalization problems, scalability issues).

I guess that is expected. If you want a book that's easy to read that can get you excited about some cool ares in software development, this book is great. If you want information beyond the introductory casual reading level, look elsewhere.

As a long time O'Reilly reader & fan, I have to say this is the best O'Reilly book I've
read in the past several years, and is now among my favorite programming books in general. This is really an applied Artificial Intelligence book in disguise, as it covers most of the core topics found amongst the top AI textbooks. I've recently read a few of the standard AI books, such as Norvig, Duda & Hart; which are thorough, but in a bad way, because they miss the forest for the trees. Your average working software developer is not going to be able to use these textbooks to create any code without investing a lot of time, or stopping long the way to get a Phd.

And this is precisely where this books shines, unlike similar books out there--Toby Segaran has managed to explain the core AI algorithms in plain language, with very readable code examples that implement a fully working example to get you started. Reading this book made me realize most of the AI that I've studied is not hard in itself, but rather the standard way AI algorithms are presented in textbooks is just terrible and obfuscated.

For example, Toby describes a fully working backpropagation neural network, with code(!) in about 9 pages. I've never seen a NN presentation better than this. There were several chapters where I couldn't help laughing at how conceptually easy a given algorithm ends up being if only you stop and explain it as simply as possible, and throw out most of the mathematical notation. That sounds obvious, but for some reason few authors think brevity helps get the point across, especially when dealing with a mathematical topic. So kudos to Toby for this, which is a major accomplishment in itself, as it's going to really help the book appeal to a much wider audience.

I also though it was a great idea to connect every topic in the book to large data sets which anyone can get off the web. This lead me to think of many other kinds of datasets to try this code on, so it's not the kind of book that you read and put away;
but rather you keep tweaking the example code(available on the book's website), adding to it and experimenting.

In all, a great book, highly recommended!

I use python as my primary programming language, when I ordered this book I was concerned it would be more about website design then AI algorithms (collective intelligence encompasses a subset of soft AI algorithms that draw upon information from various sources readily avaliable on the Internet, large document collections, etc.) I found the text to be readable with broad application in other areas including document classification systems for analyzing large amount of documents in the context of e-discovery. I would recommend this book to anyone using any-type of clustering process for review and analyzing documents and data. Taxonomic, clustering, neural networks, etc. are sold generally to the public as magic while in fact the concepts are readily accessible in this book.

Love the book, great topical review of methods with working examples. Every chapter makes you think of a dozen things you could do next.
My only reason for 4 instead of 5 stars is that the code examples are all python-based and leverage python specific features. The book title should be "Programming Collective Intelligence...with Python" although it does present a fun challenge to convert the examples to a different language (like Ruby!).

This book introduces you to a lot of useful math for web 2.0 or social based applications and brings it all the way down to code you can write and run in Python. I learned about some great python libraries out there like beautiful soup and others which are useful in more ways than just the collective intelligence aspect utilized in the book. There were even a few more elegant ways of doing something in Python that I learned through reading the code in this book. Just about every application I use could make use of the math and algorithms in this book to make using it a bit more pleasant experience. If you're a python programmer you must have this on your bookshelf, if you are a programmer that wants people to like your application you should have this book in a tattered state on your bookshelf.

Have you ever wondered how:
* Google comes up with its search results
* Amazon recommends you books/movies/music
* spam filters decide good from bad

Well, Toby Segaran not only explains these topics and more in Collective Intelligence, but he does so in a way accessible to software developers that haven't worked on machine-learning problems before. He even provides working Python code for all the algorithms.

Collective Intelligence is a great read. I could not wait to get home and get back to it -- and when I went in to work the next morning, I usually had a new idea or two of how to improve our software. I also have been implementing the most important examples in Groovy to make sure I get them.

Collective Intelligence is accessible to all practicing software engineers, but if you are a Senior Software Engineer or "better," this is a must-read. Proper application of the algorithms in this book are a great way to simplify your system and avoid getting nickel-and-dimed to death with new ways to prioritize/categorize/slice-and-dice your data.

I've worked in web development for years now. I get excited when I see new web trends and applications come out, and I love the progress we've made with mashups and the like. It's great to see what the web has become.

I picked this book up because all the examples were in Python, and I'm a big fan of python. I also liked the concept of writing mashups in Python. I expected it to be very python-centric. It was, but that wasn't what stuck out to me.

What I found was a book all about algorithms. I've been fascinated with some of the algorithms we see every day on the internet (Amazon's suggestion algorithm has been my favorite). Instead of presenting confusing math equations, or using huge words, Segaran puts examples in front of you. From online dating services, to del.icio.us trends, this book puts forward modern, real world examples of using common collective intelligence algorithms on the internet.

Anyone interested in building a mashup or web development in general should read this book.

Good book - this is very specialized and might cover information that you need. Suggest going over to B&M store to actually take a look before you buy it. Or if you can browse table of contents first either by book searching at amazon or safari.

I've always been interested in algorithm development, and was curious to see these creative techniques applied to data mining technologies. The author does a great job presenting complex material in a format that encourages hand-on experimentation in addition to providing an introductory understanding of the subject. The books is divided into chapters which focus on a specific problem, and the author walks you through techniques to solve them, from high level theory to concrete examples, often using data retrieved from online sources. The code samples were easy to follow (even without knowing Python), and numerous instructions and links were provided for libraries, data sources, etc to assist interested readers with creating their own programs or researching topics further. He even includes a summary chapter at the end, reviewing the highlights of each algorithm, along with pros and cons of the method. If you're interested in the subject, this is a good book for your shelf.

The basic idea of the book is to show different ways of using user-generated datasets to extract information and make decisions. Each chapter tackles a different problem and shows a few ways to approach it.

To give you an idea of Toby's approach, I'm going to run through my favorite chapter so far, the one on Optimization. The quick description of the problem domain should be familiar to every programmer -- solve NP complete problems like the traveling salesperson. Basically, Optimization techniques can be used in situations where (1) you can't analytically solve the problem (2) the solution set is too large to exhaustively search (3) you can express the cost of a possible solution such that better solutions have a lower cost than worse ones and (4) solutions that are "near" each other have similar values for cost.

Toby's first example is how a family can choose flights so that they arrive at the same airport from all over the country at nearly the same time and depart for home a few days later at nearly the same time. The cost function is a combination of the actual costs of the tickets and the difference in time between the earliest and latest flights. He goes on to provide a dataset of flights, describes setting up optimization problems in general, and shows the python code to implement the cost function.

Then he introduces a few optimization algorithms, implements them, and discusses their relative strengths and weaknesses. The next section applies these techniques to real data on Kayak.com using the standard Python DOM parser, minidom. The final section shows how to apply these techniques to other problems like pairing students in a dorm and network visualization. Other chapters get a similarly exhaustive treatment.

I really hope that this is the future of technical books.

This is truly a great book. I am currently learning Python and within minutes, I was running my own web crawler.

I was even able to take the sample code that uses Python (and SQLite) and modify for MySQL.

A few minutes later, I had created over one million records in my database searching for keywords.

Not many books like this so grab it while it's hot!

A+

cbmeeks

This is one of the best books I've read in the last few years. It has a very natural and easy to follow format, with simple but powerful pieces of code written in Python.
Have you ever wondered how to build a search engine? This book not only explains all the moving parts of a search system, but also builds one with the reader, step-by-step.
It is focused on the web 2.0 arena, but the ideas can be used in many other types of applications.
I highly recommend this book to learn about different techniques and to give you insight of what you can do with information in general.

Excellent book. Most interesting to me was the amount of data
available for free download. Housing prices from zillow. Historical
stock information from Yahoo. etc. This book shows you how easy it
is to download the data and analyse it.

This book is probably best for those of you who have read the theory, but are not quite sure how to turn that theory into something useful. Or for those who simply hunger for a survey of how machine learning can be applied to the web, and need a non-mathematical introduction.

My area of strength happens to be neural networks (my MS thesis topic was in the subject), so I will focus on that. In a few pages of the book, the author describes how the most popular of all neural networks, backpropagation, can be used to map a set of search terms to a URL. One might do this, for example, to try and find the page best matching the search terms. Instead of doing what nearly all other authors will do, prove the math behind the backprop training algorithm, he instead mentions what it does, and goes on to present python code that implements the stated goal.

The upside of the approach is clear -- if you know the theory of neural networks, and are not sure how to apply it (or want to see an example of how it can be applied), then this book is great for that. His example of adaptively training a backprop net using only a subset of the nodes in the network was interesting, and I learned from it. Given all the reading I have done over the years on the subject, that was a bit of a surprise for me.

However, don't take this book as being the "end all, be all" for understanding neural networks and their applications. If you need that, you will want to augment this book with writings that cover some of the other network architectures (SOM, hopfield, etc) that are out there. The same goes for the other topics that it covers.

In the end, this book is a great introduction to what is available for those new to machine learning, and shows better than any other book how it applies to Web 2.0. Major strengths of this book are its broad coverage, and the practicality of its contents. It is a great book for those who are struggling with the theory, and/or those who need to see an example of how the theory can be applied in a concise, practical way.

To the author: I expect this book will get a second edition, as the premise behind the book is such a good one. If that happens, perhaps beef up the equations a bit in the appendix, and cite some references or a bibliography for those readers interested in some more in depth reading about the theory behind all these wonderful techniques. (The lack of a bibliography is why I gave it 4 stars out of 5, I really think that those who are new to the subject would benefit greatly from knowing what sits on your bookshelf.)

This book has great examples that make the machine learning algorithms come alive.

Most chapters have instructions on how to hook up the code to web based APIs so you can get some real data to play with.

I hope there's more books like this.

I do application/systems program and this was a great introduction to a lot of new concepts and new technologies including python and graphing/analyzing data. Excellent and timely examples, flows from one topic to the next very smoothly. Very well-written and organized.

The book was great, it arrived on time for class. And it was great that the instructor let us borrow his book, to see exactly which book to purchase.
Thanks,
Lynette

Wow! This has been a great read. I have been interested in neural networks and machine learning for awhile now, but I was unsure of where to start. The author's writing style is easy to follow. The examples have been very helpful. The only complaint I have is that one of the URL's, given in the book, is broken. Otherwise, I would highly recommend the book to anyone interested in this subject matter.

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.

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.

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.

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).

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.

"Collective Intelligence" is a masterpiece on a subject that is difficult to approach unless you enjoy reading highly specialized papers.

The subject is extremely interesting in the anytime/anywhere information age where data mining technologies and smart algorithms are shaping the way we experience our "digital" lives.

It is hard to get up from the chair and walk away from the computer before finishing reading (and experiencing by coding/playing around with the so interesting examples in the book), like in a good thriller you just want to devour the information to the end and start experimenting yourself with all the new skills that this book will bring for sure to most of its readers.

I have to say that this has been a 2in1 book as I was not familiar with Python. I am amazed now by the variety of libraries and the power of this language to do almost everything your imagination can bring. Both subjects together in the same book have made me to enjoy so much.

Also, as a PhD candidate on Evolutionary Computation I cannot be happier. At last I can see a book with an excellent pragmatic approach and a "hands-on" philosophy which is, in fact, the best way to learn almost anything.

Very useful indeed and it will definitely become permanent part of my most select library.

Segaran has done an excellent job of explaining complex algorithms and mathematical concepts with clear examples and code that is both easy to read and useful. His coding style in Python often reads as clearly as pseudo-code in algorithm books. The examples give real-world grounding to abstract concepts like collaborative filtering and bayesian classification.

My favorite part is how he shows us code (gives it to us!) that goes out into the world, grabs masses of data and does interesting things with it. The use of a hierarchical clustering algorithm to dig into people's intrinsic desires in life as expressed in zebo is worth the price of the book alone. The graph that shows a strong connection between "wife", "kids", and "home" but a different connection between "husband", "children", and "job" is IMHO just fascinating.

Gems like that make this book worth reading cover to cover. After that it can happily hang out on your shelf as a reference anytime you need to build something to mine user data and extract the wisdom of crowds.

"Programming Collective Intelligence" is a great book. I took a college course on data mining and this book really would have come in handy.

From a "hands-on" programming perspective, the information on the useful libraries in python for crawling, parsing RSS feeds, python drawing, and accessing popular RESTful APIs are really valuable. The code samples are well documented and rather timely. I think Toby has done an amazingly cogent job of demonstrating the nuts and bolts of implementing the plethora of data mining and AI-related concepts pertinent to the field of Collective Intelligence. Additionally, I was new to Python and this book was a real eye opener.

In fact, more than just a book on Collective Intelligence, this is a really useful Python book. I learned a lot about Python reading through the examples and trying to get them to work on my laptop. (I was new to Python before this book, but have since started using Python at my work).

The author has demystified the abstract idea of Collective Intelligence and presented the concepts in an excellent programming language choice in Python. Most of the topics covered are things most developers just hear about. Taking a college course on Data Mining or Artificial Intelligence may expose one to the ideas, but I have never encountered a book that introduced the topics covered in "Programming Collective Intelligence" in a way so intuitive and familiar to the programmer. Distilling all of the topics into a set of very useful Python script really illustrated how practical and available these concepts really are in ones daily work. I will definitely make use of Toby's book.