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Collective Intelligence in Action

Satnam Alag PhD · 4 HN comments
HN Books has aggregated all Hacker News stories and comments that mention "Collective Intelligence in Action" by Satnam Alag PhD.
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Amazon Summary
There's a great deal of wisdom in a crowd, but how do you listen to a thousand people talking at once? Identifying the wants, needs, and knowledge of internet users can be like listening to a mob. In the Web 2.0 era, leveraging the collective power of user contributions, interactions, and feedback is the key to market dominance. A new category of powerful programming techniques lets you discover the patterns, inter-relationships, and individual profiles-the collective intelligence--locked in the data people leave behind as they surf websites, post blogs, and interact with other users. Collective Intelligence in Action is a hands-on guidebook for implementing collective intelligence concepts using Java. It is the first Java-based book to emphasize the underlying algorithms and technical implementation of vital data gathering and mining techniques like analyzing trends, discovering relationships, and making predictions. It provides a pragmatic approach to personalization by combining content-based analysis with collaborative approaches. This book is for Java developers implementing Collective Intelligence in real, high-use applications. Following a running example in which you harvest and use information from blogs, you learn to develop software that you can embed in your own applications. The code examples are immediately reusable and give the Java developer a working collective intelligence toolkit. Along the way, you work with, a number of APIs and open-source toolkits including text analysis and search using Lucene, web-crawling using Nutch, and applying machine learning algorithms using WEKA and the Java Data Mining (JDM) standard. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.
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You can't take enough maths and statistics classes. Machine Learning - these days at least - is very maths and statistics oriented. Linear Algebra is big, so make sure you have that covered.

If you want to get your toes in the water a bit with ML, there are some great ML libraries that encapsulate some of the popular algorithms. Mahout[1], Weka[2] and Mallet[3] are popular in the Java world,

A lot of folks use Python for ML as well, and there are some good libraries there.

The R language is also popular in ML circles; as is C++. If you learn some combination of Java, Python, C++ and/or R, you'll be in good shape from a programming language standpoint.

Check out http://mloss.org/software/ also.

Some good books to get started with include:

Algorithms of the Intelligent Web[4]

Programming Collective Intelligence[5]

Collective Intelligence In Action[6]

Stanford make a great series of lectures[7] available online that you might find useful.

[1]: http://mahout.apache.org/

[2]: http://www.cs.waikato.ac.nz/ml/weka/

[3]: http://mallet.cs.umass.edu/

[4]: http://www.manning.com/marmanis/

[5]: http://www.amazon.com/gp/product/0596529325

[6]: http://www.amazon.com/Collective-Intelligence-Action-Satnam-...

[7]: http://see.stanford.edu/see/lecturelist.aspx?coll=348ca38a-3...

There's really good books on Data Mining at Borders, and a recent bunch of "collective intelligence" books; the Manning books are excellent, but you have know java; you probably also want to install Weka and R, look at the Python suite (numpy, scipy, matplotlib), tools like that; also look up the ~107 (!) algorithms that Bellkor used for Netflix comp.

http://www.research.att.com/~volinsky/netflix/

http://www.amazon.com/Programming-Collective-Intelligence-Bu...

http://www.amazon.com/Collective-Intelligence-Action-Satnam-...

http://www.amazon.com/Algorithms-Intelligent-Web-Haralambos-...

showerst
Strong upvote on that book list. If you want to learn from scratch, start with the "Programming Collective Intelligence" book, then move to "Collective intelligence in action", they're both quite good.

If you've covered and understand the material in both, you're probably ready to consider moving to some of the more academic texts.

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