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Отзывы учащихся о курсе AI for Medical Diagnosis от партнера deeplearning.ai

4.7
звезд
Оценки: 1,697
Рецензии: 365

О курсе

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....

Лучшие рецензии

RK

2 июля 2020 г.

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

26 мая 2020 г.

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!

Фильтр по:

251–275 из 365 отзывов о курсе AI for Medical Diagnosis

автор: Deleted A

21 апр. 2020 г.

Great Course!

автор: Mustak A

21 мар. 2021 г.

great course

автор: Haiyun H

1 окт. 2020 г.

ありがとうございました。

автор: RICARDO A F S

6 авг. 2020 г.

Great course

автор: Anamitra M

19 июля 2020 г.

Great course

автор: ahmed g m

21 мая 2020 г.

great course

автор: 鲁伟

12 мая 2020 г.

great course

автор: wonseok k

24 февр. 2021 г.

fantastic!!

автор: Keerthi G

18 июля 2020 г.

Excellent

автор: YangBochen

18 апр. 2021 г.

Terrific

автор: Kamlesh C

15 июня 2020 г.

Thankyou

автор: Santiago G

24 апр. 2020 г.

Thanks!

автор: salisu A

20 июня 2021 г.

Thanks

автор: Bùi M N

14 мая 2021 г.

T

H

a

n

k

s

автор: Jeff D

8 нояб. 2020 г.

Thanks

автор: Abraham G

6 дек. 2021 г.

great

автор: Ajay K

25 апр. 2020 г.

W

O

R

T

H

автор: ROBERT A R V T C

28 авг. 2020 г.

nice

автор: Bikash k K

15 июля 2020 г.

good

автор: DR. M E

20 мая 2020 г.

Good

автор: Ana C S B

6 июня 2020 г.

.

автор: Nirav S

25 мая 2020 г.

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.

автор: Sameer V

31 дек. 2020 г.

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.

автор: Erwin J T C

8 мая 2020 г.

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.

автор: Carlo F

23 нояб. 2020 г.

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.