To get intuition and the right foundation read Society of Mind. For me the book is more about thinking in terms of computation which is what (IMO) ML is about instead of statistics (of which is important to know too!).
Now practical: I think the best way to learn is pick an algorithm & representation and implement it in your favorite language. Bonus if you have your own language to work with.
I would start looking into Decision Trees first, implement them and then implement some use cases(, which follow after implementing them). Do this for other approaches, like ANN, which you can have it beat you at checkers which is strangely satisfying.
But keep in mind Minsky. I think he is like Archimedes doing "Calculus"-type approaches without fully realizing. Maybe you could be Newton?
Now practical: I think the best way to learn is pick an algorithm & representation and implement it in your favorite language. Bonus if you have your own language to work with.
I would start looking into Decision Trees first, implement them and then implement some use cases(, which follow after implementing them). Do this for other approaches, like ANN, which you can have it beat you at checkers which is strangely satisfying.
But keep in mind Minsky. I think he is like Archimedes doing "Calculus"-type approaches without fully realizing. Maybe you could be Newton?