Here are the ones that I found most useful, by Andrew Ng [1] and Daphne Koller [2] on Coursera.

[1] https://class.coursera.org/ml-003/lecture [2] https://class.coursera.org/pgm-003/lecture

I've heard nothing but good reviews of the online version of the class. I took the class at Stanford (and actually worked on the system mentioned in the article) and I found its content to be useful. I believe that the online version contains less theory but this is not necessarily a bad thing if all you want is an introduction.

I've also found reading papers to be illuminating: often the first article about a given classifier is fairly well written and accessible if you have a strong background in math.

This is also a useful thing to keep in mind: http://scikit-learn.org/stable/tutorial/machine_learning_map...

This looks clipped in my browser, but you can see the original diagram here: http://peekaboo-vision.blogspot.com/2013/01/machine-learning...

I really liked the Caltech course. I didn't spend too much time trying the Stanford one, but I got the sense that this one had more of an overarching theoretical framework that every new concept was explained in terms of.

http://work.caltech.edu/telecourse.html

Been meaning to watch these sometime:

http://homepages.inf.ed.ac.uk/vlavrenk/iaml.html