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Stanford University

Fundamentals of Machine Learning for Healthcare

Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. But we will never realize the potential of these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles. This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare. The course will empower those with non-engineering backgrounds in healthcare, health policy, pharmaceutical development, as well as data science with the knowledge to critically evaluate and use these technologies. Co-author: Geoffrey Angus Contributing Editors: Mars Huang Jin Long Shannon Crawford Oge Marques In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

Status: Convolutional Neural Networks
Status: Health Policy
BeginnerCourse14 hours

Featured reviews

KK

5.0Reviewed Feb 5, 2021

The course was inspiring and useful for a future career! Congratulations to Professor Matthew Lungren and Assistant Professor Serena Yeung! :)

FW

5.0Reviewed May 7, 2023

Excellent introduction to ML and AI in the context of healthcare. Loaded with information without being overwhelming

GM

5.0Reviewed Nov 11, 2020

Completing this course has given me a solid foundation and confidence to engage at a deeper level with AIML in health, both as a student and exponent thereof.

AJ

5.0Reviewed Sep 8, 2020

Amazing course teaching the innumerous opportunities in the healthcare sector and the application of AI in the same. Beautifully drafted course with intriguing tutorials and exercises.

KK

5.0Reviewed Aug 31, 2023

Mathew and Serena are excellent. I enjoyed the module. The subject matter is very dense but made palatable by these two experts. Thanks and kudos to them!

SM

4.0Reviewed Sep 21, 2021

the quality of videos was great. week 4 till week 7 have some hard to learn problems, it is better to make it more clear and easier to understand.

SI

5.0Reviewed Feb 24, 2024

I really enjoyed learning from this course . Helped me increasing my knowledge and clearing my ambiguities and misconceptions regarding the AI in healthcare.

DN

4.0Reviewed Jan 2, 2021

Good course but the language needs to be simpler. Sometimes simple facts are complicated with the use of high pedigree words that don't really add much to conveying the overall message.

MM

5.0Reviewed Oct 7, 2020

An excellent course for professionals with healthcare background, specially for those who want to test the water before diving deep into AI in Healthcare.

RR

5.0Reviewed Jan 1, 2021

Top quality teaching from Matthew and Serena, thank you! Engaging throughout the whole 7 weeks, best if you have some previous knowledge in ML.

BH

4.0Reviewed Oct 3, 2021

There are maybe too much scenes without slides, if you explain with slides combined, it would be more easy to understand and follow

RM

5.0Reviewed Jul 3, 2024

The course is very specific and relatably tough to follow. But it gives all the baseline of Machine Learning and its implication in health care. Thank you coursera for the course

All reviews

Showing: 20 of 145

Jun Wei Tan
5.0
Reviewed Sep 26, 2020
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5.0
Reviewed Sep 27, 2020
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3.0
Reviewed Nov 8, 2020
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5.0
Reviewed Dec 31, 2020
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Reviewed Sep 9, 2020
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Ri
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Reviewed Jan 2, 2021