Build the machine learning foundation for healthcare demands! Learn how to turn complex clinical data into models that drive decision support, early warning, diagnostic assistance, and personalized treatment insights. This course equips you with practical machine learning skills for real-world healthcare analytics. You will apply supervised, unsupervised, and temporal modeling techniques that match common healthcare data realities and clinical use cases. You’ll learn to frame clinical prediction problems, construct features from structured and time-based data, and develop classification and regression models for healthcare settings. You’ll also discover patient subgroups using clustering and dimensionality reduction and interpret patterns in patient populations. Across the course, you’ll focus on interpretability, robustness, and healthcare-appropriate evaluation metrics tied to clinical risk and patient safety.
In hands-on labs, you’ll build a Readmission Risk Classifier, cluster patients for phenotype discovery, visualize populations with dimensionality reduction, engineer temporal features for an early warning model, and compare models using ROC, PR, calibration, and threshold-based utility analysis.
