Machine Learning for Data Analysis

Very shallow course.

I had just finished Andrew Ng's Machine Learning class and was looking for something to continue to build my knowledge in ML when I bumped into this course. It looked very interesting, as it covered algorithms that were not explored in Ng's course, like decision tree and random forests.

However, I was very disappointed when I found out that the actual class material expands very little on what is already written on the syllabus. It's an almost complete blackbox approach to the algorithms, with just some explanation of what is going on.

Most of your time in this course will be spent watching someone call functions X, Y, Z from libraries A and B to run the algorithm of the week, at best with some intuition of what goes in the blackbox and what comes out. And if something goes wrong, you'll have no idea why.

TL;DR
Skip this one, just read the syllabus and google the algorithms or watch youtube videos about them.