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

1,533 ratings
336 reviews

About the Course

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. As an AI practitioner, you have the opportunity to join in this transformation of modern medicine. If you're already familiar with some of the math and coding behind AI algorithms, and are eager to develop your skills further to tackle challenges in the healthcare industry, then this specialization is for you. No prior medical expertise is required! This program will give you practical experience in applying cutting-edge machine learning techniques to concrete problems in modern medicine: - In Course 1, you will create convolutional neural network image classification and segmentation models to make diagnoses of lung and brain disorders. - In Course 2, you will build risk models and survival estimators for heart disease using statistical methods and a random forest predictor to determine patient prognosis. - In Course 3, you will build a treatment effect predictor, apply model interpretation techniques and use natural language processing to extract information from radiology reports. These courses go beyond the foundations of deep learning to give you insight into the nuances of applying AI to medical use cases. As a learner, you will be set up for success in this program if you are already comfortable with some of the math and coding behind AI algorithms. You don't need to be an AI expert, but a working knowledge of deep neural networks, particularly convolutional networks, and proficiency in Python programming at an intermediate level will be essential. If you are relatively new to machine learning or neural networks, we recommend that you first take the Deep Learning Specialization, offered by and taught by Andrew Ng. The demand for AI practitioners with the skills and knowledge to tackle the biggest issues in modern medicine is growing exponentially. Join us in this specialization and begin your journey toward building the future of healthcare....

Top reviews

Jul 2, 2020

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

May 26, 2020

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!

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226 - 250 of 334 Reviews for AI for Medical Diagnosis


May 10, 2020

Perfect course

By rather

Jun 2, 2021

Great course.

By Arif R

Sep 8, 2020

Excellent !!!

By Julio E F

Jun 21, 2020

Great course!

By Franco T

Apr 21, 2020

Great Course!

By Mustak A

Mar 21, 2021

great course

By Haiyun H

Oct 1, 2020


By Ricardo A F S

Aug 6, 2020

Great course

By Anamitra M

Jul 19, 2020

Great course

By ahmed g m

May 21, 2020

great course

By 鲁伟

May 12, 2020

great course

By wonseok k

Feb 24, 2021


By Keerthi G

Jul 18, 2020


By YangBochen

Apr 18, 2021


By Kamlesh C

Jun 15, 2020


By Santiago G

Apr 24, 2020


By salisu A

Jun 20, 2021


By Bùi M N

May 14, 2021







By Jeff D

Nov 8, 2020


By Ajay K

Apr 25, 2020







Aug 28, 2020


By Bikash k K

Jul 15, 2020


By DR. M E

May 20, 2020


By Ana C S B

Jun 6, 2020


By Nirav S

May 25, 2020

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.