[disgruntledphd2]

If you are willing to do the work, Frank Harrell's Regression Modeling Strategies is a pretty good introduction to a lot of this.

It's written for a very different set of problems than typical ML, but it has lots of really good advice for practical problems in data analysis and prediction (which is another term for ML).

Mostly people learn this stuff by experience. Find a dataset, choose a predictor, filter, clean and massage your data till you get better metrics/understanding (preferably both). Rinse, repeat on many different datasets and problems, and you'll know how to do this.