
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...
Algorithms of the Intelligent Web by H. Marmanis
Collective Intelligence in Action by Satnam Alag
- 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)
-
- 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)
If you've covered and understand the material in both, you're probably ready to consider moving to some of the more academic texts.