first, you need to grok the basics http://academicearth.org/courses/machine-learning but data mining can't be learned from a passive standpoint. You need to find a large dataset http://aws.amazon.com/publicdatasets/ and try to do something with it. I've been pleased with http://neuroph.sourceforge.net/ for a lot of the stuff I do.

Data mining is a lot of statistical work and machine learning, so if your stats knowledge is rusty, basic or non-existent, then I would suggest you read up on that.

SCPD at Stanford offers a certificate program in data mining

http://scpd.stanford.edu/public/category/courseCategoryCerti...

(if the above is broken, http://tinyurl.com/stanford-graduate-certs )

Ben Fry's dissertation 'Computational Information Design' [http://benfry.com/phd/] is a great start. He breaks down many of the skills needed to succeed at information design and processing.

He's recently released Mastering Data with O'Reilly, which is essentially an expanded second addition to his dissertation.

I'd recommend you to start with data visualization, intute how mean, median, variation don't capture the data, then move onto normal distribution approximation of dataset, then linear regression and finally bayesian classification/probability models.

Only after you get a good grasp on basics should you move to advanced topics such as neural networks, SVMs, etc.