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Learner Reviews & Feedback for AI for Medical Prognosis by DeepLearning.AI

4.7
stars
553 ratings
95 reviews

About the Course

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. Machine learning is a powerful tool for prognosis, a branch of medicine that specializes in predicting the future health of patients. In this second course, you’ll walk through multiple examples of prognostic tasks. You’ll then use decision trees to model non-linear relationships, which are commonly observed in medical data, and apply them to predicting mortality rates more accurately. Finally, you’ll learn how to handle missing data, a key real-world challenge. These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. This course focuses on tree-based machine learning, so a foundation in deep learning is not required for this course. However, a foundation in deep learning is highly recommended for course 1 and 3 of this specialization. You can gain a foundation in deep learning by taking the Deep Learning Specialization offered by deeplearning.ai and taught by Andrew Ng....

Top reviews

JD
Jun 4, 2020

I am a medical image analysis enthusiast. But I always wonder why I can't I combine other patient details for extending it's application. Sure this course is awesome. I really loved it !!

BM
Apr 21, 2020

This course was great and more challenging that I have expected. More focus on statistics and survival data which is important for prognosis. Course has a good flow and valuable content.

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1 - 25 of 95 Reviews for AI for Medical Prognosis

By Scott V

Apr 29, 2020

The videos were too short and seem to be missing some critical information that was included on the tests. The assignments, from a program structure perspective, were hard to follow. Lastly, I was a little disappointed on the lack of AI concepts included.

By Boštjan M

Apr 22, 2020

This course was great and more challenging that I have expected. More focus on statistics and survival data which is important for prognosis. Course has a good flow and valuable content.

By Yogesh G

Apr 22, 2020

The course builds upon the statistical formulations for various prognostic models . As far as I experienced, the course requires no background in deep learning or machine learning and neither in medicine, so if you're interested in the topic you don't have to worry about any pre-requisite. Unlike other courses you will not be using neural networks or deep learning algorithms, however the assignments uses a lot of real life data for prognosis tasks, so it's still an exciting course to build upon you own prognostic models.

By James H

May 2, 2020

Excellent course! Real world data and robust models. Of particular value was the implementation of the SHAP feature interpretation algorithm as applied to ensemble models.

By Srinivas A N

May 19, 2020

Excellent course that teaches you complex concepts in an easy to understand manner. The assignments are challenging, while at the same time teaching you advanced stuff!

By Ashutosh A

May 23, 2020

Really helpful course if one wants to understand the application of machine learning in the field of medical prognosis,i.e., to predict the medical condition from the given data. The instructor is really good at explaining things and the videos as short, crisp, and covers all major aspects needed to be understood by the student. I would recommend to undertake this course if you want to work in the medical applications of ML and familiar with the basics of ML.

By MAHANTHI A R

Jul 20, 2020

A well structured courses, which is very fun to go through. The lectures are very short and the learning is more practical and application oriented. The tutor is very knowledgeable and navigates us through some of the tough concepts in a logical way. The course is free from medical jargons and thus is very recommended for CS people interested in applying their knowledge to medicine, without any prior knowledge of Medicinal jargons

By Yashveer S

Apr 26, 2020

This was a wonderful course, I am now able to see how this course relates to the actual medical field, where we go from diagnosis to treatment and finally prognosis.

By Harold S

Apr 27, 2020

Thank for this course, I really enjoyed it! It was well structured and the explanations are very clear! The labs are amazing, as they really help understanding the models and how to use them. Some recommendations:- I really like the way that the course is structured and that you are beginning with naive and easy solutions to come up with better ones, but sometimes it wasn't explicit enough that they where naive solutions while you were presenting them. Therefore it was a bit frustrating because during the first videos, i was always like "Oh man why is he doing that? I have a better way!"... only to realize that you were about to present something better afterward - Too many pure computation questions in the quiz... I find it useless to have questions with a formula and the data and we only need to understand how the formula is applied... It is too easy and it doesn't help understand deep meanings and explanations.- The quizzes are not mandatory to pass the course and they should be!- When a model is presented in the video, you could also explain how it is going to be trained, because sometimes i didn't know if some weights that you were talking about were learned or were just hyper parameters (ex: in basic models exemples of week 1)Another example is at final week, in the video 'survival tree', you manually split the data but you don't explain how the survival tree will be trained, now that we don't have a labelled outcome 1 or 0 associated with each patient.- It is not stated if the models and solutions that you are presenting are the state of the art, or if we should investigate more.- It would be nice, for each course, to have an exemple of solution that is already used by some hospitalsDon't hesitate to reach out to me if some of my recommendations are not clear enough... My english isn't perfect :) Thanks again, i'm looking forward to do the last course!

By Francesco G F

Jun 29, 2020

It seems to me the course is at least half about statistics, not AI. Nevertheless, the content is interesting and well explained. The coding exercises and projects may makegood starter code for your own projects.

It is clearly explained, there is even too much hand-holding. When the instructor shows a (very basic) integral and reassures us that even if we don't know what it is, we will be alright, he is pushing it too much. Anyone who hasn't seen an integral like that before is not in the audience of the course anyway.

The projects are very interesting and relevant, it is a pity they have been dumbed down in the execution to the point that we are required to fill in missing lines at specific places. I believe ungraded exercises should build up to the project, but then for the project we should have more free reign. Again, beginning programmers have no business attending this course anyway.

By Vinayak N

Aug 21, 2020

Awesome course! Anyone looking to get an insight into how to perform studies which are preemptive, especially in healthcare must go through this course. The instructor is lucid, delivers content fantastically and there's sufficient supplementary resources as well. Only thing which I would have liked more is more practise assignments. Otherwise for content as a whole, it's a 5/5.

By Moustafa A S

Aug 4, 2020

the coding assignments were not that hard sadly, but the knowledge about techniques and methods and formulas to interpret the prognosis is very helpful

By Umberto S

Aug 2, 2020

Some more support about the assignments in the forum or in the slack channels could be really helpful to better understand the exercise solutions.

By Hossein A

Oct 4, 2020

This is a nice introductory course for some of the machine learning applications in medical prognosis. However, it lacks depth in ML algorithms and it focuses more on data engineering and preparation for medical applications. As a tip try to read some of the citations or google the concepts (random forests, ...) in medium or other data science blogs.

By Mario L

Aug 22, 2020

I feel that video and other materials are not preparing well for the quizzes.

By Zeeshan A

Jun 29, 2020

Thank you Pranav Rajpurkar and Andrew Ng for this amazing specialization! Thank you deeplearning.ai! Thank you Coursera!This specialization covers application of AI algorithms for: medical diagnosis of patients using chest X-Rays and 3D MRI brain images; prognosis of patients using survival models; and medical treatment recommendation models.The lectures were brief and comprehensive, the quizzes included toy problems to test the grasp over the mathematical formulas, and the assignments were simple and covered implementation of most of the concepts taught in the courses.

By Philip J S

Sep 23, 2020

We'll its not definitely an Andrew Ng level of simplifying complex topics and explaining the intuitions behind. But the author Pranav did a great job on compiling useful topics that will sort of guide the practical and cutting edge (i.e. SHAP library) applications of AI in Healthcare. I used it as a curriculum guide on which topic to further study and deep dive. Each videos was just like 1 minute high level explainer for a topic, but it should give you a head start on what to focus on. Overall, I this is very satisfactory!

By Shubham R

Jun 24, 2020

Absolutely fantastic course! Believe me you'll never get disappointed of a course/specialization by the "deeplearning.ai" team, the same goes for this one too. Pranav has done an excellent job explaining the intricacies involved with medical data and has very well extrapolated these in accordance to the deep learning concepts.

By Onuigwe V

May 28, 2020

The medical prognosis is a comprehensive course for predicting and analysing future condition of patient for medicine. Highly recommended for anyone with interest in medical research. Even if you don't understand the concepts, re-visit it again. This is called learning with patience as 'patient'.

By Navodini W

May 24, 2020

This course is well organized and have a good flow, that helps to understand all the facts. The assignments are also good and have a lot to learn. Thank you very much for providing a platform for students to learn this area, AI in medical applications.

By Jingying W

Jun 28, 2020

Great course! I really enjoyed the part of the illustration of how to handle the right-censored patients and have understood how the chain rule of probability is applied. It would be better if the python assignments have more exercises for practice ;)

By Santiago I C

Apr 22, 2020

Great course! Good introduction to survival models in medicine, explained with a good medical background. Pranav is a good instructor. Great using python and data science packages as they are used everywhere nowadays. Time well-spend! Congratulations!

By Rahul R

May 24, 2020

Very well introduced to basic concepts and nuances regarding application of AI in Prognosis. This is an exciting field to work in. Thank you Team DeepLearning.ai for this wonderful course. I am eagerly waiting for the 3rd course in the series.

By Aanand

Jun 22, 2020

Learnt a lot of New concepts which from medical and actuarial sciences which can be applied in different fields. Course delivery is very good and right amount of theory is balanced with equally good assignments.

Thanks a lot !!

By Francis J M

Nov 22, 2020

Absolutely, recommendable! For a short course, it covers a lot! However, it is not for absolute beginners. I will share this profound knowledge with my students! Thank you so much! Take care, everyone!