Interesting content, but superficial

a little difficult

good

I liked very much the first course of this specialization, but the second one is a waste of time. Too much of medical heuristics that doesn't transfer to other fields, and will be forgotten in a week after completing this course.

The part of the course is repeating simple algebra operations from grammar school. Also grading of python labs is based on using specific command instead of validity of results. The ratio of knowledge gained / price is very low.

The explaination about fundamental and how the survival model works was terrific but the practical lab is lacking the practice and guidance regarding how survival model implemented and to interpret it

This course has more to do with empirical prognosis models, nothing to do with AI.

I was disappointed by the course content, the teaching standard and the code. Things were discussed so superficially that I came away with little more than a sense of wasted time. There should have been more detailed explanations of the whys. There should have been greater exposition of the nuances. The videos felt like I was watching the introduction to a concept, but then nothing followed this introduction. The code exercises and labs were equally bad. This course provided almost zero value for the time I spent on it. I will give it two-stars because the creators obviously spent time and effort making this course and I respect that.

Good content. Bad quality control. The QC mistakes resulted in wasting hours of student time, and coursera help desk time. It almost resulted in lost income for coursera due to refund of money being my next step I would have taken.

There are a lot of bugs in the assignments. For example, I got stuck in a graded exercise for over an hour and read the util files only to realise the answer requires float and does not accept np.float64. Also, the assignments focus on petty details (like C index) but not how to code the model.