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學生對 提供的 AI For Medical Treatment 的評價和反饋

467 個評分
97 條評論


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. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine. Medical treatment may impact patients differently based on their existing health conditions. In this third course, you’ll recommend treatments more suited to individual patients using data from randomized control trials. In the second week, you’ll apply machine learning interpretation methods to explain the decision-making of complex machine learning models. Finally, you’ll use natural language entity extraction and question-answering methods to automate the task of labeling medical datasets. These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend that you take the Deep Learning Specialization....




A bit tough, but well laid and well explained.Overall the entire specialization was very good. However it misses in depth theory . But overall a very good course with practical applications



A very nice course and specialization as well. Offers so much to learn even for those who are pure machine learners.Instructors were fantastic.Assignments were challenging but excellent.


76 - AI For Medical Treatment 的 97 個評論(共 97 個)

創建者 Ignacio M S


Amazing course

創建者 MD A R A


Excellent !!!



Great course

創建者 Kamlesh C


Thank you

創建者 Jeff D



創建者 Rorisang S


Excellent. The first programming assignment is definitely challenging and long! The other two are shorter but are still exciting enough as they show the actual application of AI in medicine. I loved the course. Just a formality not to give 5 stars

創建者 Carlo F


Many interesting things are teached in this course, such as interpreting ML and DL models. Yet i don't feel the exams and the notebooks really make you ready to use these skills on the field, it's just an introduction. 4 stars rating.

創建者 Kevin N


The assignment for the first week was out of scope for the course in my opinion. It was too much focused on a good handling of pandas which is rather difficult for people who are not experts in pandas

創建者 Gaetano M


Outstanding hands-on machine and deep learning eHealth projects with real-life applications.

I wish I could download the support material for this course

創建者 Amit P


Weeks 2 and 3 were excellent! The week 1 programming assignment was tedious and even the quiz was a repeat from course 2.

創建者 Neel K


The assignment was very heavy. It was better to have some practical case studies to understand the implementation steps.

創建者 Sherif M


Please check out my comprehensive review on LinkedIn. Thank you!.

創建者 Asif S M


Unable to use it on real datasets.

創建者 Kiran U K


Unique awesome course

創建者 bala K K


Thanks coursera

創建者 Mark L


I thought the course was well-taught and interesting, but I felt that it was more of an Introduction -- Here are some things you can do with AI and ML techniques in the context of Medicient -- rather than a detailed explanation of how the techniques work and how to use them in practice, so probably more valuable for Medical professionals than AI/ML specialists. It would be great to have some follow-on courses that get deeper into the technical details; the Coursera Deep Learning Specialization is a great example.

In general, the programming exercises were valuable and engaging, but I have a particular gripe with the grading: In some cases, I had to spent quite a bit of time making micro-adjustments to my program text to satisfy the rather picky criteria of the grader, including one case were I had to remove spaces between tokens in an expression in order to pass. I really think the criterion for grading should be correctness of results rather than conformance of the program text.

創建者 Louis C


This name of this course is in my opinion wrong. It is much more about model tuning and evaluation than medical treatment in itself. Though it gives very useful and numerous techniques for this purpose, the assignments are quite boring, especially implementing the gridsearch in week 1 (quite out of the scope of the course, and much too long) or copying/pasting Keras functions in week 3.

The videos are however once again very clear and interesting.

創建者 Adam M


The lecture videos are great, but the Jupyter labs are entirely convoluted.

創建者 Jaime A C B


It was not as explained and clear as previous ones

創建者 Roberto S


Many concepts need further explanations as Random Forest Clasificator.

Programming assignments have many easy tasks but other very difficult as creating a class the assignment of week 1.

In general programing assignments require much more time than shown, between 10 to 15h for those not familiarized with game theory. This is a bit frustrating.

創建者 Edoardo S