For what it's worth, I think it's important to start with interesting problems. My first interaction with ML was an implementation of Naive Baye's for classifying spam, borrowing much ideas from PG's A Plan for Spam from scratch (ie no libraries). This is what got me really interested in the field, much more than randomly picking up topics- there are just so many areas to choose from. Another approach would be to read up on standard supervised learning techniques and just observing how the parameters for these algos behave on datasets. something like a Weka really comes in handy if you wish to focus on analyzing behavior of such techniques first. Best of luck!