I think my answer is a bit different. The interesting/hard part of ML is the algorithm behind it all. It is important to get a really good sense of exactly what

I think my answer is a bit different. The interesting/hard part of ML is the algorithm behind it all. It is important to get a really good sense of exactly what the technique is doing. So I would not use python, or any computer language if you are a complete beginner to a technique. I would just work it out with a toy example and pen and paper. Make your own little decision tree, or work out a bayesian probability for a given set. The "problem" with a library like, say, Sk-learn is that it does the "work" for you (sorry about the heavy use of scare quotes), but you may not know what it is doing well enough to analyze the output. My two cents.