Fundamentals of Machine Learning for Healthcare
Learn essential machine learning concepts and applications in healthcare, from basic principles to advanced neural networks and clinical implementation.
Learn essential machine learning concepts and applications in healthcare, from basic principles to advanced neural networks and clinical implementation.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full AI in Healthcare Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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English
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What you'll learn
Understand ML principles in healthcare
Evaluate healthcare ML applications
Implement clinical ML best practices
Assess ML model performance
Develop healthcare ML strategies
Skills you'll gain
This course includes:
5.8 Hours PreRecorded video
19 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 8 modules in this course
This comprehensive course explores machine learning fundamentals and applications in healthcare settings. Students learn key concepts from basic principles to advanced neural networks, focusing on healthcare-specific challenges and solutions. The curriculum covers supervised and unsupervised learning, deep learning architectures, evaluation metrics, and best practices for implementing ML in clinical settings.
Why machine learning in healthcare?
Module 1 · 1 Hours to complete
Concepts and Principles of machine learning in healthcare part 1
Module 2 · 1 Hours to complete
Concepts and Principles of machine learning in healthcare part 2
Module 3 · 2 Hours to complete
Evaluation and Metrics for machine learning in healthcare
Module 4 · 1 Hours to complete
Strategies and Challenges in Machine Learning in Healthcare
Module 5 · 1 Hours to complete
Best practices, teams, and launching your machine learning journey
Module 6 · 1 Hours to complete
Foundation models (Optional Content)
Module 7 · 1 Hours to complete
Course Conclusion
Module 8 · 1 Hours to complete
Fee Structure
Instructors
Leading Innovator in AI and Medical Imaging
Dr. Matthew Lungren is an Associate Professor of Medicine in the Department of Radiology at Stanford University and Co-Director of the Stanford Center for Artificial Intelligence in Medicine and Imaging. His research, funded by the NIH and NSF, focuses on the application of artificial intelligence and deep learning in medical imaging, precision medicine, and predictive health outcomes. Dr. Lungren's impactful work has garnered attention from major media outlets such as NPR, Vice News, and Scientific American, and he is a sought-after speaker at national and international scientific conferences on AI in healthcare. With a strong educational background that includes an MD from the University of Michigan and a Master of Public Health from the University of North Carolina, he has significantly contributed to advancing AI technologies in clinical settings. Dr. Lungren is also recognized for his teaching efforts, including his popular course on AI in Healthcare on Coursera, which aims to make complex topics accessible to a broader audience. Through his innovative research and commitment to education, Dr. Lungren continues to shape the future of healthcare through artificial intelligence.
Innovator in Biomedical Data Science and AI Applications
Dr. Serena Yeung is an Assistant Professor of Biomedical Data Science at Stanford University, with courtesy appointments in Computer Science and Electrical Engineering. She also serves as the Associate Director of Data Science for the Center for Artificial Intelligence in Medicine & Imaging and is affiliated with Stanford’s Clinical Excellence Research Center. Dr. Yeung's research focuses on developing advanced computer vision, machine learning, and deep learning techniques to interpret a wide range of visual data, including medical images, video captures of human behavior, and cell microscopy images. Her commitment to education is reflected in her graduate-level lectures on deep learning in computer vision, which have garnered over a million views online. Before her current role, she was a Technology for Equitable and Accessible Medicine (TEAM) Fellow at Harvard University. Dr. Yeung holds a PhD from Stanford University and has contributed significantly to the field through her leadership of the Medical AI and Computer Vision Lab at Stanford, as well as her participation on the NIH Advisory Committee to the Director Working Group on Artificial Intelligence.
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4.8 course rating
466 ratings
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