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.
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We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare.
The course will empower those with non-engineering backgrounds in healthcare, health policy, pharmaceutical development, as well as data science with the knowledge to critically evaluate and use these technologies.
What You Will 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.
Course 3 of 5 in the AI in Healthcare Specialization.
Syllabus
WEEK 1: Why machine learning in healthcare?
WEEK 2: Concepts and Principles of machine learning in healthcare part 1
WEEK 3: Concepts and Principles of machine learning in healthcare part 2
WEEK 4: Evaluation and Metrics for machine learning in healthcare
WEEK 5: Strategies and Challenges in Machine Learning in Healthcare
WEEK 6: Best practices, teams, and launching your machine learning journey
WEEK 7: Course Conclusion
