Examines data mining perspectives and methods in a healthcare context. Introduces the theoretical foundations for major data mining methods and studies how to select and use the appropriate data mining method and the major advantages for each. Students are exposed to contemporary data mining software applications and basic programming skills. Focuses on solving real-world problems, which require data cleaning, data transformation, and data modeling.
Machine Learning in Healthcare: Fundamentals & Applications
Machine Learning in Healthcare: Fundamentals & Applications
Instructors: Sonya Makhni
Access provided by ExxonMobil
1,721 already enrolled
Gain insight into a topic and learn the fundamentals.
10 reviews
Beginner level
No prior experience required
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
- Predictive Modeling
- Random Forest Algorithm
- Healthcare Industry Knowledge
- Model Training
- Clinical Experience
- Health Technology
- Unsupervised Learning
- Artificial Intelligence and Machine Learning (AI/ML)
- Machine Learning Methods
- Model Evaluation
- Machine Learning Software
- Machine Learning
- Machine Learning Algorithms
- AI Integrations
- Responsible AI
- Applied Machine Learning
- Data Mining
- Deep Learning
- Artificial Intelligence
- Diagnostic Tests
Details to know
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Assessments
23 assignments
Taught in English
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There are 4 modules in this course
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Reviewed on Feb 5, 2025
It was fine. The only complaint about the food is the cookie needed to be heated up and the lines were too long.
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