Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrat...
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Aug 18, 2010
Michael
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Nov 06, 2007
Steve
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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 di More...
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 More...
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Oct 16, 2010
Matthew
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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 wid More...
The book covers a wid More...
Oct 18, 2009
Tom
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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 More...
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 More...
Nov 10, 2011
Wael Al-alwani
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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
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Sep 14, 2009
Amar
rated 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 More...
- 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 More...
Aug 14, 2009
Isk
rated it: 
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...
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...
Jun 25, 2008
Adam
rated 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 (supp More...
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 (supp More...
Sep 24, 2009
Leif
rated it:
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,
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Jun 21, 2010
Noah
rated 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 More...
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 More...
Aug 13, 2011
Derek
rated it:
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.
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.
Jan 01, 2009
محمد
rated it:
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.
Aug 15, 2011
Ben
rated it:
This is a great book to learn directed applications for software development. If you want to learn the principles of how Google, Digg, twitter, Last.FM, and any other Web2.0 website works, this is the book to read. There is working example code written in Python which is not optimized, but it's a great start into this world. Programming Collective Intelligence is also a great overview of how to use the Python language properly for data mining, database API, looping, lambda functions and class
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Jul 02, 2011
The emphasis of the book is on applications rather than theory, which is what you would expect from a machine learning book published by O'Reilly. The applications are interesting and implemented in python.
Jan 31, 2011
Marek
rated it:
basic intro to machine learning, very readable, but eschews mathematics - that can be good or bad, depending on your background
PS: my bad, the mathematics (aor a little bit of it) is in the appendix
PS: my bad, the mathematics (aor a little bit of it) is in the appendix
Feb 16, 2009
Jeff
rated it:
Very interesting look at the algorithms behind recommendations and trending as they apply to web2.0
Jan 10, 2012
Amit
rated it:
One of the good books and highly useful for making recommendations in websites nowadays.
May 25, 2011
Denis
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It's amazing book with very simple explanation most of modern approaches in data mining
Jan 22, 2011
Amazing book - shows how easy it is to implement machine learning & complex intelligence services
Jul 31, 2008
Mike
rated it:
This book is a great introduction to the algorithms that power things like Google's search engine, Amazon's suggestions, and other web sites that use collective user input to extract useful information. There is a great mix of code, statistics, and AI in this book-- it isn't a light fluffy treatment.
Feb 10, 2009
Efsavage
rated it:
Excellent primer on the techniques modern sites are using, and users are starting to expect.
Dec 21, 2007
Dave
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Seriously, wow. If you have ever been intimidated by any informational retrieval technologies, or just wanted a clear explanation of how things work -- this book is fantastic. Anyone who can explain k-means clustering clearly in less than 5 pages is absolutely brilliant.
Oct 17, 2008
Brian
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One of the best computer programming books that I have ever read. It teaches you concepts, new ways of solving problems. It is very relevant to today's enviroment (web, mashups, etc). Examples are in Python.
Jul 29, 2008
Jeremy
rated it:
Offers impressive insight into the technologies behind Web 2.0 websites (like this one). Lots of great algorithms and sample implementations. A very usable book, very helpful in several of my current projects.
Nov 14, 2007
Pete
rated it:
Well done. It's A.I. 101, but with working code samples in Python that are as clear as pseudo-code. It was a quick refresher for me that I thoroughly enjoyed. The algorithm appendix is very helpful as well.
Aug 15, 2010
Alex
rated 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.
Oct 12, 2010
J.R.
rated it:
This was a great introduction to the subject. The only reason I didn't give it 5 stars was I felt some examples were very python specific so they didn't really apply to me so much.
Aug 27, 2008
Dara
rated it:
I think it might be a bit complicated for novice readers but overall it's great explaning concepts with very good examples. Cookbook for social web developer.
