Had higher expectations. Concepts not well and clearly explained. Notebooks bugged (we are actually warned about it), but even so not so interesting. Plan of the Course not so rational: why include the one section about model parameters on its own, rather than for each model.

I give it a 3 as the Instructor is smily and engaging, but it's a 2.5 mark (I have done another ML MOOC on another concurrent platform about the same topic, and the quality was much higher)

Excellent course, I was looking for a course which didn't explore advance math or go into the specifics of a particular ML method but which focuses on the main differences among then and teach about the whole process of M, this is the best course for that.

The whole specialization is extremely useful for people starting in ML. Highly recommended!

Great course! I received so much useful information from AMII.

Excellent.

Teach you practical stuff that other courses don't.

Explained things clearly

week two was heavy

This course covers lots of important ideas and knowledges for Machine Learning practitioners. It is definitely nice to deal with topics such as grid search or scikit-learn, but I think the course only covers these topics in a nutshell, it is more superficially discussed. If you are interested in Machine Learning, you should definitely bring your own motivation to dive deeper into those topics.. Also, Dr. Koop speaks very very fast though.. I attended courses by Andrew Ng, his courses provide a way better comprehensibility for listeners. The notebooks are a bit weird, very easy to understand and are hence not challenging. If you really want to understand the algorithms deeply, I don't think this course is the right one. But all in all, I completed the course, but I don't think I was able to understand everything by taking the course only.

Good coverage of the topics in supervised learning. However, lacks depth in some of the concepts.