Not too easy. Not too difficult.
Course Overview:
I strongly recommend the course. It covers most modern topics on machine learning. It not only introduces the algorithms themselves but also the mathematics and intuitions behind them. The proofs are very rigorous. They are definitely helpful both for a job interview and for research purporse (as a starting point).
Course highlights:
The best part is that Andrew gives us not only the knowledge but also the precious experience he got from so many year's research and work.
Hours per week:
3-5 hours
Advice for students:
Be sure to derive the algorithms by yourself offline.
This class was tough.
Course Overview:
This is a great introductory course in machine learning, but covers a lot more material than required for a basic course. You can get lost pretty quickly if you don't follow the lectures closely; you might feel like they have cramped way too many topics for one quarter. But if you're interested, go ahead, it's a wonderful course!
Course highlights:
Andrew Ng really gives an intuitive perspective for most topics. Unsupervised learning techniques were the highlight.
Hours per week:
9-11 hours
Advice for students:
Be prepared to do a lot of math; proving theorems is a major part of the course. Your linear algebra skills will be very important for this subject, so make sure you brush up on those before taking it.