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.

Fundamentals of Machine Learning for Healthcare
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Fundamentals of Machine Learning for Healthcare
This course is part of AI in Healthcare Specialization


Instructors: Matthew Lungren
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What you'll 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.
Skills you'll gain
- Healthcare Industry Knowledge
- Artificial Neural Networks
- Health Informatics
- Artificial Intelligence and Machine Learning (AI/ML)
- Model Evaluation
- Generative Model Architectures
- Machine Learning Methods
- Healthcare Ethics
- Medical Science and Research
- Deep Learning
- Applied Machine Learning
- Machine Learning Algorithms
- Responsible AI
- Statistical Machine Learning
- Data Ethics
- Model Training
- Supervised Learning
- Machine Learning
- Reinforcement Learning
- Health Policy
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Reviewed on Sep 20, 2025
As you said in the goodbye video, you made this with love.
Reviewed on May 7, 2023
Excellent introduction to ML and AI in the context of healthcare. Loaded with information without being overwhelming
Reviewed on 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.




