📚 Syllabus & Course Curriculum
AI & Machine Learning in Healthcare
Module-by-module breakdown of Predicting Efficiency (Exergy + Machine Learning), from foundations to a certified capstone project.
Outline
Explore Energy vs. Exergy using the First & Second Laws • Map Power Cycle components – boilers, turbines, condensers • Implement CoolProp in Python to compute enthalpy & entropy • Ingest turbine sensor data and calculate Exergy Destruction
Outline
Define features and targets for efficiency prediction • Prepare data splits for training and testing • Build a RandomForestRegressor model and train it • Evaluate predictions using Mean Absolute Error
Outline
Deploy XGBoost for high‑accuracy forecasting • Generate feature‑importance charts to explain model decisions • Visualize actual vs. predicted exergy destruction • Save the trained model for real‑time deployment
Earn government-registered certification in Predicting Efficiency (Exergy + Machine Learning)
e-Certificate and e-Marksheet issued on successful completion.
