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

4.7
1,705 個評分
366 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....

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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!

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251 - AI for Medical Diagnosis 的 275 個評論(共 366 個)

創建者 Deleted A

2020年4月21日

Great Course!

創建者 Mustak A

2021年3月21日

great course

創建者 Haiyun H

2020年10月1日

ありがとうございました。

創建者 RICARDO A F S

2020年8月6日

Great course

創建者 Anamitra M

2020年7月19日

Great course

創建者 ahmed g m

2020年5月21日

great course

創建者 鲁伟

2020年5月12日

great course

創建者 wonseok k

2021年2月24日

fantastic!!

創建者 Keerthi G

2020年7月18日

Excellent

創建者 YangBochen

2021年4月18日

Terrific

創建者 Kamlesh C

2020年6月15日

Thankyou

創建者 Santiago G

2020年4月24日

Thanks!

創建者 salisu A

2021年6月20日

Thanks

創建者 Bùi M N

2021年5月14日

T

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a

n

k

s

創建者 Jeff D

2020年11月8日

Thanks

創建者 Abraham G

2021年12月6日

great

創建者 Ajay K

2020年4月25日

W

O

R

T

H

創建者 ROBERT A R V T C

2020年8月28日

nice

創建者 Bikash k K

2020年7月15日

good

創建者 DR. M E

2020年5月20日

Good

創建者 Ana C S B

2020年6月6日

.

創建者 Nirav S

2020年5月25日

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.

創建者 Sameer V

2020年12月31日

The course has been designed well, learnt new terminology which I was not aware of previously when working on 2D datasets. Good introduction to 3D images. The course could be a bit more detailed, for example, since data preprocessing is very crucial, it would have been great to have had an assignment on cleaning 3D data using image registration, alignment, etc. Additional references for reading mainly books would have been nice. Finally, brief details on the type of computing power and memory is required especially for 3D images would have been very helpful. If I run the code on my laptop, I am sure it will crash, would be nice to have an idea of the requirements. Anyways, thank you for the course, very nice introduction to AI in medical field.

創建者 Erwin J T C

2020年5月8日

As a Radiologist from the Philippines who has been desperately trying to find some kind of "grounded center" for all the AI/ML topics I've been studying online, this is a really great way to consolidate what I've learned so far especially for AI applied to Radiology. I've been training models for computer vision (based on free tutorials on-line) but this has definitely given me better insight as to how those models actually work and how they come together from simple numpy arrays, to tensors, layers, and finally into compiled models.... giving me a better appreciation for how activation functions and convolutions actually fit into the development of convolutional neural networks. More power to the team.

創建者 Carlo F

2020年11月23日

The course was interesting but did not make me feel ready to apply a DL model on such data. It'a like being in a sandbox all the time: you play, you see things, then you are required to build your own, little, insignificant castle with your little basket, but no more than that. I think that real problems in AI application in this field are not about calculating sensitivity, specificicity or standardazing data, things for whom there are already functions built in libraries. I feel I know more this job, but i wouldn't be ready if i didn't know it yet before.