I think being effective in ML requires both theory, and practical knowledge you only get by doing. Andrew Ng's ML course quickly provides a base in theory. Id

I think being effective in ML requires both theory, and practical knowledge you only get by doing. Andrew Ng's ML course quickly provides a base in theory. Ideally you couple that with some empirical work. For that, I think sklearn is the best starting point (assuming you go down the python path). Modify some sample code and make a few simple models. Sklearn provides an excellent framework across all kinds of models (including deep learning if you use say keras.wrappers.scikit_learn), and can play well with pandas. There are lots of practical concerns that come up that are not covered in intro ML courses.