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AI for Medical Diagnosis に戻る による AI for Medical Diagnosis の受講者のレビューおよびフィードバック



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....



Jul 03, 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 27, 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!


AI for Medical Diagnosis: 151 - 175 / 263 レビュー

by MD R A

Jul 28, 2020

Very much informative with a clear explanation.

by Mohan

Jun 27, 2020

very useful for my medical imaging project

by Luca N

Apr 20, 2020

Great Course and very interesting topics!

by Khaldoon A

Jun 09, 2020

Very easy to follow and well structured

by Shubhram P

May 11, 2020

Thanks a lot for such a great course!!!

by Mário A N G

Jul 30, 2020

Very interesting and hands-on course.

by Carlos M C F

Jun 07, 2020

Very Hard and with good information

by Peter

Jul 10, 2020

very good course , highly recommend

by Lucky R

Apr 22, 2020

Thanks for this wonderful course.

by Merajul I s

Oct 09, 2020

very good and informative course

by Muhammad A I

May 07, 2020

Provide some hits in assignments

by Douglas S F

Jun 18, 2020

Ótimo conteúdo. ótimo material.

by Jingying W

May 31, 2020

nicely organized and explained!

by Shyam s K

May 06, 2020

Crisp and relevant explanations

by Muhammad U

Sep 20, 2020

Very good course for beginners

by Arturo P

May 16, 2020

Great course! learned a lot <3

by 西川 尚之

May 05, 2020

Great! We just have to learn!

by Sayed H

Aug 08, 2020

Excellent course taught well

by rudraps

Jul 19, 2020

Very good course. thank you.

by Rachit D S

May 08, 2020

Very well explained course.


Oct 21, 2020

Challenging and aswesome!

by Manikant R

Jul 31, 2020

Very interesting projects

by Anggi Z

Aug 05, 2020

Learn many skill in here

by Rodica A

Jul 15, 2020

Some Courses are good.

by Lee, B

Apr 28, 2020

Thank you very much.