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.

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 !

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.

I did not expect such good content in this course but it surpass my expectation. Its very well designed course which can engage you till the end and will definitely help you in your academics or work.

Course is very good. But I am very annoyed with the project system involved. While I am typing anything, the autosave function undoes all the changes that I made. It took me 10 to 12 tries sometimes to write a single sentence

I was disappointed with the quality of the submissions I saw and the wording of some the quizzes, which in many cases had questions which were incorrectly scored or graded. Many of the submissions I saw had maybe one or two words in each bullet point. With the rubric suggesting that if someone mentioned a specific topic we were to give the writer credit, this means a couple of half sentences was enough to pass a peer-graded "assignment".

This Coursera is a SCAM

The content was informative and very relatable to current needs in using ML in a healthcare setting. While there is technical jargon used, a beginner in ML should still be able to grasp concepts, although I think it helps to have a strong math/technical/DS foundation.

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'.

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.

A great summary pulling together the material taught in the previous 4 courses. Easy and straightforward to go through. There are a few items in the course that need to be cleaned up, like duplicate quiz questions with conflicting "correct" answers, and opening up the discussion forums.

The quizzes have errors in the answers (already reported), and peer review is buggy. Received emails saying I have feedback but unable to see. Even AI grade and feedback cannot be seen.

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

Good overview for AI in Healthcare but more reading and OTJ training will needed.

need the first courses

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.