Chevron Left
返回到 AI for Medical Diagnosis

學生對 deeplearning.ai 提供的 AI for Medical Diagnosis 的評價和反饋

4.7
1,620 個評分
353 條評論

課程概述

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 deeplearning.ai 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....

熱門審閱

RK
2020年7月2日

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

KH
2020年5月26日

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!

篩選依據:

201 - AI for Medical Diagnosis 的 225 個評論(共 353 個)

創建者 Iasonas C

2021年11月16日

Great knowledge is been provided!

創建者 lucky r

2020年4月22日

Thanks for this wonderful course.

創建者 Alexander Z

2021年4月14日

Thanks! Very intresting content.

創建者 Merajul I S

2020年10月9日

very good and informative course

創建者 Muhammad A I

2020年5月7日

Provide some hits in assignments

創建者 Douglas S

2020年6月17日

Ótimo conteúdo. ótimo material.

創建者 Jingying W

2020年5月31日

nicely organized and explained!

創建者 shyam s

2020年5月6日

Crisp and relevant explanations

創建者 Pham V V

2021年1月24日

Excellent course! Very useful!

創建者 Muhammad U U

2020年9月20日

Very good course for beginners

創建者 Arturo P

2020年5月16日

Great course! learned a lot <3

創建者 西川 尚之

2020年5月5日

Great! We just have to learn!

創建者 Sayed H

2020年8月8日

Excellent course taught well

創建者 rudraps

2020年7月19日

Very good course. thank you.

創建者 Hitesh D

2021年5月4日

Good Learning experience!!!

創建者 Elga

2021年1月28日

Great course! Many thanks!

創建者 Rachit D S

2020年5月8日

Very well explained course.

創建者 Brototo D

2021年1月18日

Sweet course! very helpful

創建者 Nosaybeh A P

2020年11月6日

Excellent!!!!

thanks a lot.

創建者 MICHAEL D S R

2020年10月21日

Challenging and aswesome!

創建者 Manikant R

2020年7月31日

Very interesting projects

創建者 Anggi Z

2020年8月5日

Learn many skill in here

創建者 Rodica A

2020年7月15日

Some Courses are good.

創建者 KUNAL S

2021年8月16日

Exceptional course!!!

創建者 Lee, B

2020年4月28日

Thank you very much.