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Fundamentals of Machine Learning for Healthcare
Coursera
Course
Unknown

Fundamentals of Machine Learning for Healthcare

Stanford University

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 will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. We will explore machine learning approaches, medical use cases,

Unknown8 weeks38,195 enrolled

About this Course

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 will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. We will explore machine learning approaches, medical use cases,

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.

Instructors

M

Matthew Lungren

Department of Radiology

S

Serena Yeung

Biomedical Data Science

Course Info

PlatformCoursera
LevelUnknown
PacingUnknown
PriceFree

Skills

Machine Learning
Convolutional Neural Networks
Machine Learning Algorithms
Responsible AI
Healthcare Ethics
Data Preprocessing
Reinforcement Learning
Applied Machine Learning
Artificial Neural Networks
Data Ethics

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