Learner Reviews & Feedback for AI for Medical Diagnosis by DeepLearning.AI

Course concept good. In fact one of the first courses with direct practical application of AI.

This and other 2 courses expect beginners knowledge of deep learning hence newcomers may find it tough. However sorely miss in depth theory of U Net and other advanced Algorithms . Videos are crisp, smart but inadequate.

going to take the next 2 courses and complete them

I learned a lot from this course. Each lab, assignments, and weekly quizzes enabled me to take a deeper dive into how these models and image processing work on medical images. It made me wear my thinking cap and think deeply into each parameters and features and what mathematical-statistical models are used for prediction and classification analysis!

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!

This course was excellent! I previously did the Machine Learning course from Andrew Ng and the CS50 AI course on Edx, and I've been trying to work on ML projects since. Specifically, I've been trying to do a lot of work on medical applications of ML, stuff like brain tumor detectors. I wanted to do this course to learn about things like U-Nets and how to evaluate my models, and this course really helped with that! I don't think I'll be completing the entire specialization since winter break is almost over and I probably won't have time, but I definitely feel very satisfied with the information I've gained so far! My one complaint is that I felt the assignments were very "hand-holdy", as in they didn't let you implement a lot of the stuff yourself. There are functions you implement, but that's mostly just really simple stuff. I think the assignments are very cool, but I think I would have preferred assignments that were less complicated but you had to do most of the work yourself so that it truly feels like you built it.

This is the best course of the specialization. The instructor created one of the best models for chest X-ray diagnosis that was the first model that beat human radiologists in detecting pneumonia (now with COVID-19 that's more important than ever). The original CheXNet model is flawlessly and simply explained so that anyone could understand it with all details served literally on a plate requiring no additional work. This is my favorite course of all DeepLearning.ai specializations! Thanks Pranav & Andrew!

Amazing course if you are interested in learning about the applications of AI in the field of Medical Diagnosis. Learned the meaning of a lot of medical terms and concepts required in this field. It was a perfect combination of interesting and challenging tasks that kept me hooked in the course from beginning all the way till the end. Highly recommended for anyone interested in this particular field of study for their research or education.

This was the most easy to understand course I think due to how it was presented and broken down into various sections. The exercies and assignements helped to clear the concept. The coding assignment could add some more information so that it would be easier to visulize 4D arrays and what to slice etc, may be add few more lines to Hint?

The course is excellent in its content. However, I would suggest a bit more rigorous setting like letting the students write the function on their own just by providing them with hints. Also, I would like to suggest that some of the utilfunctions that were provided can be given as assignments say for honors purposes.

Extremely well-written content/code and short but illuminating lectures and discussions. Good terse discussions of common metrics, issues with imbalanced datasets, and interesting ways of tackling those issues, U-Net architecture and loss functions for semantic segmentation, and exploration of medical datasets.

Thanks for gathering all the useful material for using AI in medical imaging. I come from the medical imaging background, and I can't express how useful and precise were your teaching materials. I also would like to thank the Slack support system for all the useful hints on how to solve the assignments.

Good course!! The clasification part is similar to classification in other courses (such as tensorflow course from DL.ai) but some medical basics. Good introductory and some good tips. The segmentation part is very explainatory as it's really what's needed to begin in real practice. Keep up! Recommended

It is been my dream to apply AI in healthcare. This incredible course has given me the knowledge that I need to approach medical image data from preprocessing to model development, and prediction. I also learnt some radiological and medical jargons along the way.

Thank you so much Deeplearning.ai.

The course teach me to consider some flaws that i had made back in the preprocessing step and is a good refresher of the metrics of clasification models. Also, the brain mri image segmentation assignment was very special because it serves as a starting point for input a voxel in a neural net.

It is really a great course on applying machine learning and deep learning to medical field. The video lectures are short and easy to follow. Assignments are so great. Really looking forward to take the following courses. Thank you deeplearning.ai and coursera for this amazing course.

How to store and handle the medical image datasets can also be included. As all your assignments had the readymade datasets, I feel bit difficult on the steps on how to store and import the data from our PC or Desktop when we want to apply the same techniques to our own datasets.

An excellent insight of Medical Image Processing, highly recommended for those who are working on Medical Images. Segmentation and boundary delineation was not covered in details, maybe in the follow up courses this aspect will be covered more deliberately. Thank you coursera .

Overall courses is very good though the course is short with respect to video lecture and course material the assignments are bit lengthy and tough. It would be good to have assignment code walk through as we have refer multiple outside materials w.r.to python coding.

Great starter course on AI in medical use. The course was very well structured. Definitely recommend this course to someone who is looking to apply AI in medical field. The course requires some knowledge of python programming and understanding of neutral network.

Excellent well metered presentations and quizzes. The final quiz really made you think and understand the entire process. I tried not to use the discussions, however, as the final quiz submission was getting close, reached out to Mubashar. Zeroed in on my issue!

Really good course for learning to train biased and complex image datasets. I was waiting for this course since it was first introduced on Youtube deeplearning.ai. Feeling really satisfied that the course was exactly the same level I was expecting it to be.

Excellent course which introduces key concepts of AI in medical diagnosis. Concepts are explained in a clear and effective manner for both videos and labs. Videos are extremely bit-sized and to the point. Good for beginners. One of the best courses around.

Awesome course. In such a short period of time, I got up and running on the medical image classification. Though, this is just scratching the surface, but hope to use this course base my experiments on. Thanks Andrew Ng & Pranav for starting this course.

Though I'm not from a medical background, the ideas and concepts explained in this course were very insightful and relate to problems in my domain. Great thanks to the deeplearning ai team and Coursera for putting together a wonderful course yet again.

Very detailed yet elementary introduction of AI application in Healthcare. I have taken more advanced courses in the university, and I wish to know this course way before I have taken one. I enjoyed it a lot and it helped me bridge the gap. Thank you!