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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Gain insight into a topic and learn the fundamentals.
Beginner level
No prior experience required
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
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
- Deep Learning
- Data Ethics
- Applied Machine Learning
- Healthcare Ethics
- Medical Science and Research
- Artificial Intelligence and Machine Learning (AI/ML)
- Supervised Learning
- Model Training
- Artificial Neural Networks
- Health Informatics
- Model Evaluation
- Healthcare Industry Knowledge
- Responsible AI
- Machine Learning Algorithms
- Statistical Machine Learning
- Generative Model Architectures
- Machine Learning
- Machine Learning Methods
- Reinforcement Learning
- Health Policy
Details to know
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Assessments
19 assignments
Taught in English
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This course is part of the AI in Healthcare Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 8 modules in this course
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