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 is part of the AI in Healthcare Specialization
About this Course
What you will learn
Define important relationships between the fields of machine learning, biostatistics, and traditional computer programming.
Learn about advanced neural network architectures for tasks ranging from text classification to object detection and segmentation.
Learn important approaches for leveraging data to train, validate, and test machine learning models.
Understand how dynamic medical practice and discontinuous timelines impact clinical machine learning application development and deployment.
Syllabus - What you will learn from this course
Why machine learning in healthcare?
Concepts and Principles of machine learning in healthcare part 1
Concepts and Principles of machine learning in healthcare part 2
Evaluation and Metrics for machine learning in healthcare
- 5 stars82.60%
- 4 stars14.85%
- 3 stars2.17%
- 2 stars0.36%
TOP REVIEWS FROM FUNDAMENTALS OF MACHINE LEARNING FOR HEALTHCARE
The course was inspiring and useful for a future career! Congratulations to Professor Matthew Lungren and Assistant Professor Serena Yeung! :)
This was a great course, the presenters really gave a clear view about the differences which could happen when working with health related data set. Very well done,
Excellent introductory course to understand Machine Learning in the context of Healthcare delivery
Interesting and well crafted, it is mostly at an introductory level, but accurate and with many details regarding how to apply ML to healthcare. Worth to follow.
About the AI in Healthcare Specialization
Frequently Asked Questions
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Is this activity accredited for Continuing Medical Education (CME)?
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