Data mining

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I recommend starting with weka and this great book: http://www.amazon.com/Data-Mining-Practical-Techniques-Manag...

Some other helpful books: - Data Mining, by Witten and Franke; describes basics , how to use Weka, which they wrote a couple java-based books from Manning: - Collective Intelligence in Action (by Satnam Alag) and - Algorithms of the Intelligent Web (Marmanis, Babenko)

some other helpful books: - Data Mining, by Witten and Franke; describes basics with rigor, including how to use Weka, which they wrote http://www.amazon.com/Data-Mining-Practical-Techniques-Manag... a couple java-based books from Manning: - Collective Intelligence in Action (by Satnam Alag) and - Algorithms of the Intelligen Web (Marmanis, Babenko) -

…the python suite numpy / scipy, matplotlib ----------------------------- look at all the books out now. I think Witten/Franke (authors of weka) was the first one titled "Data Mining" but there's a slew of htem, read Amazon reviews --------------------- hit the blogosphere http://www.dataminingblog.com/2009/01/30/three-new-data-mini... http://www.decisionstats.com/

You could do a purely applied approach, look at some Data Mining books, like Witten/Franke and the Weka java framwork (there's quite a few good books, check amazon reviews, ) and the assortment of methods that are applied from basic logit/probits, through clustering, SVM, neural, evolutionary programming, .

…materials (search amazon on "data mining" http://www.amazon.com/gp/product/1584504609 http://www.amazon.com/Data-Mining-Practical-Techniques-Manag... Also the Collective intelligence by Satnam alag is quite good (a lot of java code to wade through tho

… the Norvig book is very well-written and is good background for learning about ML, but I don't think it is a sufficient ML book as such. As far as ML goes, I found the "Programming Collective Intelligence" book to very readable and practical, but very light on the theoretical foundations (which is intentional, of course). I've got a copy of the Witten ML book ("Data Mining: Practical Machine Learning Tools and Techniques"), but to be honest I haven't gotten much from it yet, either: it doesn't seem to discuss SVMs in any detail, nor random forests or neural networks. But I haven't really dug into it yet.

I still think Peter Norvig's "Artificial Intelligence" book plus a good book on Data Mining (someone suggested Frank and Witten above) might deliver more value than this. As Henning pointed out above , there aren't enough pages to cover even a few of the topics properly. It looks like the coverage is fairly thin and lacking in depth, and is more of a "some basic AI algorithms for the hot topic of the day" type book. I haven't seen the book yet but the table of contents makes me suspect the quality. "Support Vector Machines" for example are fairly complex beasts. The book has one section (from what I can see on the TOC) about SVMs. Norvig's AIMA is a brilliant book precisely because it lays a solid foundation for so much of AI. And it is still a very hefty book. I somehow doubt that this is of the same caliber. But hey, I could be wrong. J…

…n was Google's invention of PageRank" Huh? PageRank dates from the late 90s. And it's implemented in a compiled language (C++, I think -- not even GC'd). And it involves elementary linear algebra. Heresy! Now, as for the book itself. It looks like it tries to cover waaay too much -- support vector machines and other forms of supervised learning, unsupervised learning/clustering, optimization and evolutionary computation, applications to collaborative filtering, along with using particular libraries. There's no way you could cover all that in 358 pages! I gave a talk on evolutionary computation to a Ruby users group and their jaws literally dropped. Web people don't give a shit about something if it doesn't involve HTTP, a programming language that's currently en vogue, JavaScript, or relational databases (really only MySQL or maybe Postgr…