Thank you so much to the deeplearning.ai team for the course!

Thanks to your work, you have been able to immerse yourself in the world of medical applications for ML.

I will highlight the following points as a criticism:

* imho, mediocre code quality: vectorization is ignored in many places without proper reasons; insufficient code encapsulation: beginners can learn bad things

* many programming tasks are more like solving riddles and require more time to solve them than contain useful information

I wish you good luck in educational field and look forward to new courses from your team.

This is the worst course in all my experience on Coursera. The material has been prepared with evident hurry and superficiality but the more disgusting thing is the total absence of assistance and support. There are evident problems with the grader, reported by several users, and no one has been giving an answer since the course startup. If this is the quality of service provided by Coursera, there is non hope that I'll engage a new course in the future.

This course was quite disappointing. Although I expected more, given the track record of deeplearning.ai, this course felt rushed, and more of a practice in good coding rather than giving any knowledge or advanced intuition into the applications of AI in medical treatment. The first assignment in particular felt like it was mostly testing the student's ability to use fancy python package syntax than anything else. Overall, I wouldn't recommend this course at the point of competition (November 2020). I think students are far better learning the AI methods described in this course on their own, and only then learn their applications in medicine if this is required for their work. At the end of the day, data is data no matter what we're looking at. This is not to say that this course cannot improve in the future. To be fair, I would actually hope it does, to keep the great standard that deeplearning.ai has gotten us used to over the years.

The whole specialization was well-designed in such a way that even an engineering graduate with very less knowledge about statistics, probability, and also machine learning could understand what was taught. All the videos were short and crisp to the point and were quite explanatory. The assignments were really good and sometimes challenging which really made me think about the solutions. Except for the week 1 quiz that was repetition from last course others were really good. The entire experience as a CS undergrad new to AI in the field of medicine was great. Hope we get to experience similar courses in due course of time. Thank you!!!

Excellent course and the specialization. I feel like I participated in a research project. Learned much, and have cool notebooks to revisit at depth.

Most pathetic course done so far !

This was a great course for learning how to understand what your model in doing. Being a data scientist in industry, this is really helpful for error analysis. Thank you to the instructors for creating this entire medicine specialization. It is one of the most practical AI courses that is out there thus far.

This is a fantastic course. I have learned so many new things about the application of AI and deep learning in medical treatment. Using the BERT model to answer questions from medical report was surreal.

I found this course very interesting and completely worth it. It made me notice how the subjects splits should be performed and I also particularly enjoyed the part in which we apply GradCAM.

Learned a lot about interpretations of both machine learning and deep learning models. Introduction to basic NLP techniques was a great start too. The overall course is really good.

While it is understandable that not many techniques are covered duo to time restriction, it will be better to include a list of reference at the end to let students know there are also other techniques to solve the problem (e.g. different methods for AI interpretability)

Some of the content in the slides are wrong and very confusing. While a textbox correcting the error is appreciated, the better way should be to correct them directly in the video by editing the video or simply re-record it, given the length of each video is quite short.

The theory is good. Like, it was the first Ehealthcare course, where someone told about SHAP and different types of missing data. But programming tasks are huge and boring. As this course is not about the deep knowledge, then why are you making us implement cross-validation in raw python when it is already implemented in standard packages? Better teach how to use it from there, IMHO. And the original code full of things like `from utils import *`, global variables, etc.

Provides a good introduction to RCT, evaluation of treatment effects, using BERT for question answering, label extraction from medical reports and also interpretation of ML models and deep learning CNN models. The videos provide a good understanding and the notebooks in the exercises and the assignments help in applying the learning, so that the learning is reinforced!

some parts of the labs not mentioned directly or sometime even indirectly in the videos, previous courses had rich lectures however this one require to add some missing parts into the videos to make sure all labs and assignment covered in a way.

Thank you so much for the quality of those course..deeplearning.ai please keep lunching such excellent courses en AI : Ai and computer Vision, AI for industry, Reinforcement Learning,etc.

A very important course. Only 4 points, because at many points I had a feeling that many things were abstracted away, and am not sure whether I'd be able to replicate them on my own.

This is more of a course on pandas dataframes and various data preprocessing and formatting tasks than really understanding the functionality of scikit-learn and tensoflow as applied to medical research. The instructors should spend more time on presenting the AI tools proper than dealing with the minutiae of of how to manipulate the data. I'm not saying that it is not important to structure and manipulate the data, but at least equal (in fact more) time should be dedicated to understanding and using the AI tools. Disappointing!

Thank you Pranav Rajpurkar and Andrew Ng for this amazing specialization! Thank you deeplearning.ai! Thank you Coursera!This specialization covers application of AI algorithms for: medical diagnosis of patients using chest X-Rays and 3D MRI brain images; prognosis of patients using survival models; and medical treatment recommendation models.The lectures were brief and comprehensive, the quizzes included toy problems to test the grasp over the mathematical formulas, and the assignments were simple and covered implementation of most of the concepts taught in the courses.

This was a very challenging course for me. However, since I'm not a professional programmer or even an expert in python, the platform and course syllabus was amazing in allowing me to learn more about AI/ML in medicine despite my predominantly medical background. It gave me a bird's eye view of things I will have to study more in learning how AI/ML is applied to medicine particularly in my field of radiology. The instructors as well as my fellow classmates were very helpful in the forums.

Great specialization for anyone interested in both AI and medicine. I really enjoyed the straight-forward videos and quizzes. You need to know a bit about AI already; not so much about medicine, and there is plenty of supplementary information available anyway. The real-life scenarios used in the assignments throughout the whole course give you a chance to try some of the newest algorithms used in medicine. Highly recommend it.

As usual, Andrew Ng and his team had done an amazing job democratizing AI education for all and this recent AI + Medicine was really a joy to learn with a passionate community. I not only learned even more but I also got connected to many like minded folks from all over the world in a time that COVID-19 impacted so many sheltering in place (but learning more AI !!!).

I am Master in Information Health Engineering graduated from Universidad Carlos III de Madrid. Basically we apply Machine Learning and Deep Learning techniques to medical images. Anyway this course has been a great experience despite I already has some background. All the materials and the professor have great quality!

Thanks a lot for your efforts!

Here I want to thank this course and all the mentors. Thank you so much to give me the chance to learn what I want.

This is a great course, which contains the new methods and important AI models for medicine. It's also very intensive and a great Challenge to finish it.

Thank you so much again!

It was really great to learn from this course, as well as it was from whole specialization! There are a lot of practical examples, interesting assignments, Pranav concise and clear explanations...really, during whole specialization I haven't found any bad thing! Thank you guys for great work!

This course was very informative. All three topics were revelatory: Topics were explained well and the associated programming assignments were challenging learning experiences . Along the way, I have read related papers and have found the material to be state of the art. Well done!