I took the original Stanford AI course and got an immense amount out of it. I got a poor score since I did not have a great deal of time to work out all the problems.. But what I really enjoyed about the course was getting a grasp of the new techiques, way beyond the conventional statitistcs..
I'm looking for a way to get to the next level. The course really did not provide a good reference for reviewing the subject. The big book by the course authors (Artificial Intelligence: A Modern Approach by Norvig and Russell) (which I have) is too complex for me.
I try to do all my work in Python and I am making some headway in getting the tools, Sci-py, scikit learn, NLTK. Pandas (am currently reading Data Analysis with Python, by Mckinney, excellent). and more.. But I'm in need of references to help me leap from the conceptual level to actual implementation , including proper tool selection and problem selection and definition, that is, sort of a cook book approach.
This question may be too open ended,, but it expresses my dilemma. The whole space is quite open ended, and I'm looking for references to help me navigate through it.
What references (accessible) might you suggest?