If you're interested, there are a whole host of fun and useful machine learning techniques that are actually not as hard to understand and apply as they sound. The best introductory book that I know of is Programming Collective Intelligence, which is surprisingly clear, if a little vague on the theory:
Naive Bayesian classifiers are just one of the more popular types; others include Support Vector Machines (SVMs), decision trees (and their relatives, random forests), and a bunch more. If you'd like to play around with some, Weka is good open source software for this:
http://oreilly.com/catalog/9780596529321
Naive Bayesian classifiers are just one of the more popular types; others include Support Vector Machines (SVMs), decision trees (and their relatives, random forests), and a bunch more. If you'd like to play around with some, Weka is good open source software for this:
http://www.cs.waikato.ac.nz/ml/weka/