.

Overall it is still a good course and worth doing but I won't expect to be able to clear a job interview in medical machine learning based on this course. It touches many nice topics such as what to do if data is unbalanced, different metrics about evaluating the models. However the part about MRI segmentation seems very rushed. I would consider this as a very basic course and the student would have to spend significant personal time exploring on his/her own to really understand the concepts presented in the class. It wasn't easy for me to get help on some programming assignments when I got stuck a. Moreover, when I didn't get a perfect score on the programming assignments, I don't know where I made the mistakes, which makes it impossible to correct them.

As a Radiologist from the Philippines who has been desperately trying to find some kind of "grounded center" for all the AI/ML topics I've been studying online, this is a really great way to consolidate what I've learned so far especially for AI applied to Radiology. I've been training models for computer vision (based on free tutorials on-line) but this has definitely given me better insight as to how those models actually work and how they come together from simple numpy arrays, to tensors, layers, and finally into compiled models.... giving me a better appreciation for how activation functions and convolutions actually fit into the development of convolutional neural networks. More power to the team.

Overall, it is a good decision to take the course. Although it focuses on practical aspects of the AI in medicine, it falls short explaining the basic CNN architecture for image segmentation or classification. That said if you wish to fully take advantage of the course, spend some time understanding some of the key functions available in the util.py scripts which can be accessed through the notebooks. There, you could benefit from the course and learn interesting implementation stuff if you feel like the assignments are too practical.

This is an amazing course for people who know AI and want to know about it's applications in the healthcare industry. I had fun learning from the instructor Pranav who is concise and delivers lessons comprehensively. Overall an amazing course. Could have asked for more assignments and hands-on stuff, hence I'm being conservative on granting 4-stars only...

Very good course on applying AI for image-based medical diagnosis. Some things that could be improved are : 1. adding content relevant to using AI in non-image based diagnosis 2. could be made more comprehensive with more applications, exercises and theoretical content by extending course duration to a longer time

The video segments could be made longer to incorporate more information on how the modeling is done. A lot of new information was thrust into the weekly exercises. It would be better if the weekly exercises were a test of what we had learnt. A great course on the whole, anyway. The instructor was very clear.

Introduces the fundamentals of using AI for medical diagnoses. Concepts are clearly explained and the assignments are well framed. More lectures regarding subtle concepts like MRI Image registration and calculation of confidence interval would have made the course more interesting and comprehensive

The course serves as an introduction to AI applications on medical diagnosis. The assignments are easy. However, video lectures are missing some minor concepts that suddenly appear in the programming assignment. It is recommended to take this course after you took Deep Learning Specialization.

A very well structured course that covers most of the practical design challenges of deep learning applications in healthcare sector. A good foundation for people who want to pursue a career as a Machine Learning Engineer for medical diagnosis and/or computer vision.

Great course! Although the coding exercises focus more on lower level details of matrix manipulation, and not on the parts for selecting a model, building and training it. Most of the model related code is provided if form of utility code or as pretrained weights.

Hello,

I enjoyed taking this course. It would be great if assignments could be debuged, I tried downloading the assignments to debug using vscode but some parts of the assignments(datasets or some functions) were not there to be downloaded.

Thank you

This was a great course for getting a high-level understanding of AI's applications in medical diagnosis.

The only issue is that the assignments are auto-graded which, coupled with bugs, can make submitting assignments very frustrating.

A good course to understand the use of Deep Learning and AI in Medical Diagnosis. In this course, you can understand different ways to segment and analyze the images of brain tumors and X-Rays.

Use cases selected were really nice, Videos should carry more detail technical aspects and could be bit more lengthy and Assignments should consider multiple options to solve given problem

too hard

too little explanation in the exercises,

definitely not for beginner,

this is an expert class course,

even an experienced student, who's familiar with tensorflow might struggle a bit

The assignments are extremely simple; mostly just implementing an equation in Python. The rest of the notebooks are basically readings. Maybe give a little more coding practice.

The course is build in a way makes it easy to learn. I liked how the assignments had been built and the way of grading quizzes

I think we need a special course for U Net

Very abstracted and high level course, no "intution" presented compared to Machine Learning of Andrew Ng; Nevertheless, still a great course for AI in Healthcare

This course is a good starter for applications on AI on medicine. I enjoyed. But I felt there can me little more explanations instead of a very short videos.

The course content was great and all theoretical concepts were clearly explained but, the instructions in the programming assignments were a bit unclear.

Nice course to learn basics of machine learning as well as get your hands dirty with application of artificial intelligence to medical diagnosis.

it's good, I expected it to be richer, but I guess there's no much development in that area to teach currently, I hope it will evolve with time

There were a lot of problems when I was completing the programming task in week 3. I Think that many points are not sufficiently explained.

So nice and complete. May be some programming assignments need to be more clearer or explained, but over all; great course. Thank you.