📚 Syllabus & Course Curriculum
AI & Machine Learning in Healthcare
Module-by-module breakdown of Interpretable Machine Learning for Scientific Research and Discovery, from foundations to a certified capstone project.
Outline
Explore the role of ML in scientific discovery • Prepare scientific datasets and perform train‑test splits • Build baseline models with Scikit‑learn and evaluate reliability
Outline
Apply built‑in and permutation feature importance techniques • Create diagnostics visualizations with Yellowbrick • Interpret influential variables in scientific datasets
Outline
Generate global and local explanations with SHAP • Visualize SHAP summary plots and individual prediction impacts • Translate model explanations into concise scientific insights
Earn government-registered certification in Interpretable Machine Learning for Scientific Research and Discovery
e-Certificate and e-Marksheet issued on successful completion.
