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Course Description

In the healthcare sector, patient data is abundant. Machine learning can transform this data into a powerful tool for prediction and analysis. In this course, you will explore supervised and unsupervised learning, two key machine learning approaches that can help you maximize your data’s potential. Before addressing healthcare challenges with machine learning, it's essential to begin with high-quality data. You'll examine and practice the key steps to clean and prepare raw data, ensuring it's ready for effective machine analysis.

Once you’ve mastered these data preparation processes, you’ll be ready to apply machine learning to healthcare analysis. You’ll use supervised learning techniques to predict whether a patient is likely to experience sepsis. You’ll also leverage unsupervised learning methods to identify similar subtypes within a large group of patients. By the end of the course, you’ll realize how machine learning can improve efficiency for medical professionals and personalize patient care.

Students must have intermediate proficiency in Python programming and machine learning to succeed in this course.

You are required to have completed the following courses or have equivalent experience before taking this course:

  • Designing Digital Healthcare Tools
  • Data Management in Healthcare

Faculty Author

Fei Wang

Benefits to the Learner

  • Examine supervised and unsupervised learning algorithms and discuss how to apply them in healthcare
  • Apply supervised learning algorithms in clinical decision support systems
  • Apply unsupervised learning algorithms in stratified patient management

Target Audience

  • Data scientists
  • Medical and health services managers
  • Database and IT data architects
  • Data engineers
  • Digital transformation managers
  • Clinicians with experience in informatics
  • Biomedical and clinical informatics fellows
  • Aspiring medical database managers or administrators

Applies Towards the Following Certificates

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Enroll Now - Select a section to enroll in

Type
2 week
Dates
Jul 08, 2026 to Jul 21, 2026
Total Number of Hours
14.0
Course Fee(s)
Regular Price $999.00
Type
2 week
Dates
Sep 30, 2026 to Oct 13, 2026
Total Number of Hours
14.0
Course Fee(s)
Regular Price $999.00
Type
2 week
Dates
Dec 23, 2026 to Jan 05, 2027
Total Number of Hours
14.0
Course Fee(s)
Regular Price $999.00
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