A good and thorough introduction to this exciting field which is full of unrealised potential for healthcare and medicine. For busy clinicians like me, I found the online, learn-at-your-own pace model very helpful and convenient, and well worth the affordable price. Most importantly, you DO NOT need to learn about coding or python. But I would advise you follow up on the recommended readings prescribed at the end of each week's talk. Of course, there is a bias towards American healthcare in Module 1, but this makes for interesting reading and an appreciation of the weaknesses and strengths of US health system. I only hope that regular updates on Machine Learning could be provided every 2 years or so to past participants, similarly online, as this field is ever-evolving and changing. I would not mind paying for it.

If you are still not too familiar with the concepts learnt, this is a good chance to recap what was learnt that is mostly focused on Courses 3 and 4. Doing this course will help reinforce your learning.

Getting AI specialization Stanford University is very amazing and effective to start your AI careers. Thank you for all Stanford university lecturers, Thank you Coursera for everything !

Amazing experience, such in depth learning to understand how to approach data problems in healthcare settings and where they can actually be of benefit.

Thank you very much now I can also code having acknowledge of everything that is important. Greetings from Peru :)

Really enjoy the Capstone projects with wonderful peer-reviewing. Would recommend.

An excellent course to venture into the field of 'AI in medicine'.

Great real world exercises in order to apply knowledges of the other courses. Some questions required clinical background.

Interesting case study of the current COVID data in building AI models around them. I took the first version of the class and it has a few rough edges (grading mistakes, odd questions, missing instructions, data formatting). Even with the rough spots, the class was a thought provoking experience that required the background of the courses provided, but it also required some outside AI education and experience to come up with answers. Although I have some differences in the approach chosen by the instructors on some topics such as bias, the material is invaluable.

I learned a lot in this course. The AI in Healthcare specialization covers diverse topics with a very deep introduction to Machine Learning, Deep learning, and better still AI. I really want to say thank you to all the Stanford university lecturers, and thank you Coursera for everything and wonderful for a well-done job.

Excellent instructors! The course provided a solid foundation with broad coverage of the topic of AI in Healthcare. It was appropriate for both medical clinicians and AI developers to enable them to come together with understanding to better develop and implement valuable AI tools for future improvement of healthcare.

Very well structured course. Easy to understand for those without a backgraoud of bioinformatics. And of course great mentors

I really enjoyed this course as it was applied learning of all I learned during the previous courses of the specialization.

The quality of peer review exercises was good and the content of the reading material was well understood

The course covers diverse topics and need very deep knowledge of Machine Learning and AI

Nicely Framed and Executed in a simple language so anyone can catch up earliest.

Stanford lives up to their reputation of doing good course work.

There are 9 peer review tasks, but they are not obligate ;)

Great program and excellent learning platform!

Solid start for completely beginner

Very good course. worth our time

Excellent introduction!

Excellent course

Great team !!

Some technical issues with the quizzes and assignments (ie one assignment had no question, just an answer). Some fellow students cheated by copy-pasting in the correct answer after evaluating peers. Still, I liked that you had difficult in-depth questions even though we haven't done any ML projects or hands on coding.