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
38,660 already enrolled
625 reviews
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
- Convolutional Neural Networks
- Health Policy
- Deep Learning
- Data Ethics
- Reinforcement Learning
- Model Evaluation
- Healthcare Industry Knowledge
- Machine Learning
- Healthcare Ethics
- Data Preprocessing
- Artificial Intelligence and Machine Learning (AI/ML)
- Artificial Neural Networks
- Applied Machine Learning
- Responsible AI
- Machine Learning Algorithms
Details to know

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There are 8 modules in this course
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Status: PreviewCleveland Clinic
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Status: PreviewNortheastern University
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Reviewed on Feb 6, 2021
The course was inspiring and useful for a future career! Congratulations to Professor Matthew Lungren and Assistant Professor Serena Yeung! :)
Reviewed on May 8, 2023
Excellent introduction to ML and AI in the context of healthcare. Loaded with information without being overwhelming
Reviewed on Nov 12, 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.

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