it was better to have more detail of loss functions for segmentation

As a case study for deep learning, this course helps us a lot.

They started diving deeper towards the end of the course.

Need to more clarify the notebook content in videos.

G​ood & practical, but could've been more advanced

Really nice and well-explained

Last assignment was insane.

The problem is quite easy

The course is too basic.

excellent course

goog think

finally

The course is useful but the grading is terrible. In case of testing the code based on test cases, the grader looks into the code and only passes the code if it's written in a way consistent with the hints. This means that vectorized computations are out and one has to implement loops by hand. Furthermore, since its not clear what the grader is complaining about, one ends up wasting a lot of time if one really cares about "completing" the course. Furthermore, despite a lot of complaints the instructors have not fixed this issue.

What I am finding in most Coursera courses: 1) Important topics are just glossed over in often < 3 minutes. That itself make me wonder if the courses are worth the price even.

2) Code assignments seem to take most time figuring out what the provided code does, and how I am expected to fill in a few blanks. And after completion I am far from being able to, for example, build a basic x-ray classifier from beginning to end.

As other reviews have mentioned, though the course introduces important concepts for evaluating models in a medical context (confusion matrix, ROC curves), the concepts and exercises were too basic and surface level. Keep in mind the medical context is solely from the point of view of medical imaging. The autograder was also buggy and I was unable to start new topics in Discourse to ask questions about it.

This is interesting topic and I learnt how these things are done in medicine. However, from technical point of view, there are many issues. Bugs, typos, unexplained terms (dear learner, now please calculate background ratio) make this course messy and leaves the taste of "rushed product of corona crisis".

The course is very shallow. It explains in detail some simple concepts like Sensitivity and Specificity and then immediately touches complex topics like image recognition architectures, without much explanation. The course materials are unclear and the auto-grader is buggy.

Instructions in the graded assignment did not have clear instructions. Sometimes, correct implementation was graded 'incorrect' by the grader. Also, videos of the ROC curve was not clear about why it is needed or what does it say about a model.

Hi Sir/Madam,

i took this course then realised it is beyond my understanding. I am a grade 12 student . please help me to cancel this ,so, i can take another course which can benefit me.

i will appreciate your help.

thanking you

Too much task was given but less explanation. It was really hard to complete all the tasks. It would be better if easiest tasks are given or more explanation with huge explanation.

Expected content that would be new but found content which I was already familiar with. Disappointed a little on that. Course could have some more interesting and new content.

Good instructor but concepts were not taught in-depth. The assignments gave only a superficial understanding of the subject and cannot prepare one for working in the industry.

The course touches on several aspects of ML for medical. However, the content seems too little and narrow. Only a few cases and architectures are explored.

Would like it if the projects were more in-depth. We should understand the end-to-end pipeline: from preprocessing to deploying in production