> This table of contents is beginning to seem very familiar (I've done Google's internal machine-learning course and looked at several other courses/tutorials o

> This table of contents is beginning to seem very familiar (I've done Google's internal machine-learning course and looked at several other courses/tutorials on the net), but what I'm wondering is: how do you do the more basic operations of feature and parameter selection? I have thought about this problem for a long time. Initially, I used to think it was a mere matter of finding clever Machine Learning algorithms that could auto-magically detect features for you. There are others who think it is a matter of throwing a bunch of subject matter experts into the problem and building feature vectors. I have worked with both philosophies of people. I think the answer lies somewhere in the middle. And it is a fucking hard problem. You need to do some clever algorithmic work. Maybe use a PCA or your favorite dimensionality reduction trick but also be clever enough (mathematically that is) to realize when PCA is brittle and when not to use it. You also need to have enough understanding of the subject to be able to ask the right questions from your subject matter experts. It is easy to get five people in a room and ask them to describe a song. But it is much harder to find out the right way of asking them: Is this song the kind of song that you would dance to while drunk on three beers in a podunk town in the Mid West? That requires experience to get that sort of intuition. The only thing I can recommend (pun unintended) is to keep solving these problems, focus on the domain(s) that you are deeply passionate about. Don't spread yourself too thin and make yourself to be a general purpose Machine Learner. That is the only way to get to building beautiful end to end products.