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Thank you

Faboulous

Awesome!

Thanks!

Thanks!

G​reat

Thanks

good

Unfortunately, I would like to admit that a quality of Andrew Ng's courses declined since he personally stoped working on it... For example, I enjoyed very much all those side steps in material which Andrew did in explaining and giving an intuition to the things directly not related to the subject. He is a person with very wide expertise and this seemingly not related material brings most of the enjoyment I bring home from his courses. Now it is gone... The course is still good, but now it lucks its magic ...

Andrew, please come back ! :) You still have RL unexplained and many other things )))

Hi,

Great course. Maybe I missed something but the explanation to calculate the C_Index does not cover all cases and so the assignment is rather complicated. The Harell's C-Index algorithm is given here: "https://statisticaloddsandends.wordpress.com/2019/10/26/what-is-harrells-c-index/" and it helped me a lot.

Thanks again for course. See you at the next one.

I liked this course. Some of the concepts appeared somewhat abstract but I'll just have to review integration and derivatives. There was also a lot of syntax to learn in python but it was great to learn more about how to use numpy and pandas. Can't wait to learn more in course 3: AI in medical treatment.

The machine learning part is very basic and limited, and there are no deep learning related parts. But I have gained a lot of basic concepts about prognosis, including risk model, survival estimates, Kaplan Meier, hazard, etc. Overall, a decent course.

Good introduction to these materials, but it's difficult to use this level to incorporate into research. If you want to really use this material, you have to go deeper independently, which isn't much of an issue with the proper motivation.

A good overview of the key concepts, tools and techniques used in medical prognosis with interesting Jupyter notebook exercises and assignments that illustrate the applications and allow us to work hands-on with these techniques..

Good practice, but i want more hands-on assignment which focuses on how to build model from scratch, for example about COX model.

The course is well done and the content is high quality, as in the previous course of this specialization

Interesting content, but superficial

Great course. However, i miss how Andrew deconstructs everything - it completely absorbs all your curiosity. When you move to the assignments, without extra work you can fully understand how the libraries work. This however has a different approach, they absolutely open your mind up and enthuse you to do much more background work. really good stuff!

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.

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.