It was a nice course. Though it covers basics. A follow-up advanced specilization can be made. Overall, it's sufficient for beginner for an engineer trying to learn application of AI for medical field
Throughout this course, I was able to understand the different medical and deep learning terminology used. Definitely a good course to understand the basic of image classification and segmentation!
創建者 A V A•
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
創建者 Amit P•
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.
創建者 Mariathea D•
This is an outstanding course. I am a physician and this has been very helpful in bridging the knowledge gap between what I learned in other deep learning courses and the unique situation of working with medical data. I would however appreciate a deeper dive into how to work with the DICOM format.
創建者 Vishnusai Y•
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
創建者 Poh S C•
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.
創建者 Johan T•
Good course but, as often is the case, too much time was spent on fixing small errors in notebooks, such as using the "wrong" function (i.e. np.multiply doesn't work when * does due to the very specific setup of the exercise, even though they are both element-wise multiplication).
創建者 Vignesh S•
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.
創建者 Endre S•
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.
創建者 Hasti G•
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.
創建者 Chad H•
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.
創建者 Pierre G•
Great but 1) all notebooks must be moved to Tensorflow 2 and Pytorch 2) it's not a Deep Learning course but a data course (for people who want to really understand the classification/Unet models, they need to study another DL course)
創建者 Denizhan E•
Course data and related util files with reasonable explanations will make this course magnificent. I spent a lot of time figuring out differences while I try it in my local engine due to version differences.
創建者 Lee Z Y•
Pleasant pacing, very clear and concise lecture material. I was really frustrated with the final assignment though. Would be nice if the grader gives something more instructive than correct/incorrect.
創建者 ADITYA K•
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.
創建者 Kiran C•
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
創建者 Anditya A•
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
創建者 Pooja A•
A good course with challenging assignments. However, the assignments could have been a little less self explanatory and should have triggered deeper and more individualistic thinking.
創建者 Stephan P C•
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.
創建者 Ameera A•
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
創建者 Philip J S•
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
創建者 Chakkarapani V•
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.
創建者 Soham T•
The course content was great and all theoretical concepts were clearly explained but, the instructions in the programming assignments were a bit unclear.
創建者 Nikolaos N T•
Getting the right code for week3 assignment was really time-consuming - still have not found what was the error giving me the standardize function
創建者 Ravi P B•
Nice course to learn basics of machine learning as well as get your hands dirty with application of artificial intelligence to medical diagnosis.
創建者 Muhammed A•
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