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Volver a AI for Medical Diagnosis

Opiniones y comentarios de aprendices correspondientes a AI for Medical Diagnosis por parte de

1,648 calificaciones
358 reseña

Acerca del Curso

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....

Principales reseñas

2 de jul. de 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

26 de may. de 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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251 - 275 de 358 revisiones para AI for Medical Diagnosis

por ahmed g m

21 de may. de 2020

great course

por 鲁伟

12 de may. de 2020

great course

por wonseok k

24 de feb. de 2021


por Keerthi G

18 de jul. de 2020


por YangBochen

18 de abr. de 2021


por Kamlesh C

15 de jun. de 2020


por Santiago G

24 de abr. de 2020


por salisu A

20 de jun. de 2021


por Bùi M N

14 de may. de 2021







por Jeff D

8 de nov. de 2020


por Abraham G

6 de dic. de 2021


por Ajay K

25 de abr. de 2020







28 de ago. de 2020


por Bikash k K

15 de jul. de 2020


por DR. M E

20 de may. de 2020


por Ana C S B

6 de jun. de 2020


por Nirav S

25 de may. de 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.

por Sameer V

31 de dic. de 2020

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.

por Erwin J T C

8 de may. de 2020

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.

por Carlo F

23 de nov. de 2020

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.

por Kate S

15 de nov. de 2020

I really enjoyed and learned a lot from the material in this course. The lectures were clear and concise. Short lectures made it easy to retain the material. Also helpful were non-graded exercises embedded with the lectures. The graded labs were correct and had helpful hints.

The only improvement that I would want is to have the discussion forums back on Coursera and not on Slack. I found it difficult to search for similar questions on Slack and frequently ran into a limit on the number of messages I could search through.

Overall an excellent course!

por Hossein A

14 de sep. de 2020

Overall, it is a good decision to take the course. Although it focuses on practical aspects of the AI in medicine, it falls short explaining the basic CNN architecture for image segmentation or classification. That said if you wish to fully take advantage of the course, spend some time understanding some of the key functions available in the scripts which can be accessed through the notebooks. There, you could benefit from the course and learn interesting implementation stuff if you feel like the assignments are too practical.

por Francois R

4 de abr. de 2021

Good Course

I find that it is always tougher to teach when the audience is heavily segmented.

I see this course audience as:

- Medical practitioner who want to learn about ML

- ML practitioner who want to apply ML in a specific context.

I am of the second group.

The course is at its best when the topic are the most general like:

- The importance of correctly preparing the test, validation and training sets.

- Understanding the meaning of model accuracy in the real world.

But the implementation specifics are a bit dated now (April 2021).


por Вячеслав П

6 de abr. de 2021

The course is ok - after this course you will be ready for real tasks. but the course is not ideal: 1) you can not solve some tasks with different possible ways. As example in week 3 programming, you can not use np.empty, but you need to use np.zeros, cause another vay is incorrect. And the sub volume task - random crop loop with tries is no optimal way to solve it, but another way is incorrect. 2) I wanted to hear more about U-Net. 3) i think you need to report copies of your course on github

por Yunyan D

22 de ene. de 2021

Overall good. The lectures are easy to follow, but the programming assignments (especially week 3) need clearer instructions. The automatic grader also needs improvement, as the grader not only false alarms in a correct function and fails to detect errors in another function, but also requires very specific implementation (you can't implement in a different way, and you can't miss any argument) , even though the function works well and correct.