Learning AI by practice ( Perceptrons, Neural networks and Bayesian AI)

Question

I'm about to take a course in AI and I want to practice before. I'm using a book to learn the theory, but resources and concrete examples in any language to help with the practice would be amazing. Can anyone recommend me good sites or books with plenty of examples and tutorials?

Thanks!

Edit: My course will deal with Perceptrons, Neural networks, and Bayesian AI.

Answer 1

This link is a very nice forum where you can look at problems other people have and learn from that.

My recommendation would be to learn it by trying to implement the various types of learners yourself. See if you can find yourself a dataset related to some interest you have and then try and create a learner to do some kind of classification using different methods. Start with Decision Trees if that's part of your future course work since they're relatively simple, then move on to neural network.

Here is a set of sources, each one of which I recommend highly--for the quality of the explanation, for the quality of the code, and the 'completeness' of the algorithm demo.

• Least-Squares Regression (Python)
• k-means clustering (Python)
• Multi-Layer Perceptron (Python)
• Hopfield Network (Python)
• Decision Tree (ID3 & C4.5)