Chevron Left
返回到 AI for Medical Prognosis

学生对 deeplearning.ai 提供的 AI for Medical Prognosis 的评价和反馈

4.7
648 个评分
112 条评论

课程概述

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. 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....

热门审阅

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.

筛选依据:

101 - AI for Medical Prognosis 的 112 个评论(共 112 个)

创建者 Erwin J T C

May 22, 2020

I liked this course. Some of the concepts appeared somewhat abstract but I'll just have to review integration and derivatives. There was also a lot of syntax to learn in python but it was great to learn more about how to use numpy and pandas. Can't wait to learn more in course 3: AI in medical treatment.

创建者 Jintao R

Jan 17, 2021

The machine learning part is very basic and limited, and there are no deep learning related parts. But I have gained a lot of basic concepts about prognosis, including risk model, survival estimates, Kaplan Meier, hazard, etc. Overall, a decent course.

创建者 Karl J

Sep 24, 2020

Good introduction to these materials, but it's difficult to use this level to incorporate into research. If you want to really use this material, you have to go deeper independently, which isn't much of an issue with the proper motivation.

创建者 A V A

Jun 21, 2020

A good overview of the key concepts, tools and techniques used in medical prognosis with interesting Jupyter notebook exercises and assignments that illustrate the applications and allow us to work hands-on with these techniques..

创建者 Taiki H

May 14, 2020

Good practice, but i want more hands-on assignment which focuses on how to build model from scratch, for example about COX model.

创建者 Giulia C

Oct 17, 2020

The course is well done and the content is high quality, as in the previous course of this specialization

创建者 Romain G

Jan 23, 2021

Interesting content, but superficial

创建者 Nyonyintono J P

Aug 27, 2020

Great course. However, i miss how Andrew deconstructs everything - it completely absorbs all your curiosity. When you move to the assignments, without extra work you can fully understand how the libraries work. This however has a different approach, they absolutely open your mind up and enthuse you to do much more background work. really good stuff!

创建者 Irina G

Jun 23, 2020

I liked very much the first course of this specialization, but the second one is a waste of time. Too much of medical heuristics that doesn't transfer to other fields, and will be forgotten in a week after completing this course.

创建者 Martin S

May 23, 2021

The part of the course is repeating simple algebra operations from grammar school. Also grading of python labs is based on using specific command instead of validity of results. The ratio of knowledge gained / price is very low.

创建者 Prithviraj J

Dec 13, 2020

This course has more to do with empirical prognosis models, nothing to do with AI.

创建者 geoffrey a

Oct 21, 2020

Good content. Bad quality control. The QC mistakes resulted in wasting hours of student time, and coursera help desk time. It almost resulted in lost income for coursera due to refund of money being my next step I would have taken.