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.
AI For Medical Treatment
AI For Medical Treatment
This course is part of AI for Medicine Specialization
Instructors: Pranav Rajpurkar
25,003 already enrolled
Gain insight into a topic and learn the fundamentals.
4.7
(519 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 22 hours
Learn at your own pace
96%
Most learners liked this course
What you'll learn
Estimate treatment effects using data from randomized control trials
Explore methods to interpret diagnostic and prognostic models
Apply natural language processing to extract information from unstructured medical data
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
3 assignments
Taught in English
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This course is part of the AI for Medicine Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course
In this week, you will learn: How to analyze data from a randomized control trial, interpreting multivariate models, evaluating treatment effect models, and interpreting ML models for treatment effect estimation.
What's included
12 videos4 readings1 assignment1 programming assignment3 ungraded labs
In this week, you will learn how to extract disease labels from clinical reports, and also question answering with BERT.
What's included
10 videos1 assignment1 programming assignment3 ungraded labs
In this week, you will learn how to interpret deep learning models, and also feature importance in machine learning.
What's included
9 videos4 readings1 assignment1 programming assignment3 ungraded labs
Instructors
Instructor ratings
4.7 (100 ratings)Offered by
Recommended if you're interested in Machine Learning
London Business School
University of Leeds
DeepLearning.AI
Johns Hopkins University
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Learner reviews
4.7
519 reviews
- 5 stars
78.46%
- 4 stars
15.38%
- 3 stars
3.84%
- 2 stars
1.34%
- 1 star
0.96%
Showing 3 of 519
AL
5
Reviewed on Jun 23, 2020
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
RR
5
Reviewed on Sep 16, 2020
Wonderful course to learn the real application of AI in the medical field. Wonderfully explained every difficult concept with a simple explanation.
IG
5
Reviewed on Jun 26, 2020
Excellent course and the specialization. I feel like I participated in a research project. Learned much, and have cool notebooks to revisit at depth.
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Frequently asked questions
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.