Module #1 Introduction to Energy Demand Forecasting Overview of energy demand forecasting, importance, and role of machine learning
Module #2 Mathematical Foundations of Machine Learning Linear Algebra, Calculus, Probability, and Statistics for machine learning
Module #3 Python for Data Science Introduction to Python programming, NumPy, Pandas, and Matplotlib
Module #4 Exploratory Data Analysis for Energy Demand Data preprocessing, visualization, and feature engineering for energy demand data
Module #5 Introduction to Machine Learning Supervised, unsupervised, and reinforcement learning, model evaluation metrics
Module #6 Linear Regression for Energy Demand Forecasting Simple and multiple linear regression, regularization techniques
Module #7 Decision Trees and Random Forests Introduction to decision trees, random forests, and ensemble methods
Module #8 Time Series Analysis for Energy Demand Time series decomposition, stationarity, and autocorrelation
Module #9 ARIMA and Exponential Smoothing Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ES) models
Module #10 Machine Learning for Time Series Forecasting Introduction to time series forecasting using machine learning models
Module #11 Long Short-Term Memory (LSTM) Networks Introduction to LSTM networks for time series forecasting
Module #12 Convolutional Neural Networks (CNNs) for Energy Demand Applying CNNs to energy demand time series data
Module #13 Gradient Boosting and XGBoost Gradient boosting and XGBoost algorithms for energy demand forecasting
Module #14 Ensemble Methods for Energy Demand Forecasting Bagging, Boosting, and Stacking for improved forecasting performance
Module #15 Hyperparameter Tuning and Model Selection Grid search, random search, and Bayesian optimization for hyperparameter tuning
Module #16 Case Study:Energy Demand Forecasting for a Utility Company Practical application of machine learning models to a real-world energy demand forecasting problem
Module #17 Energy Demand Forecasting with Uncertainty Quantifying uncertainty in energy demand forecasts using machine learning models
Module #18 Energy Demand Forecasting for Renewable Energy Integration Machine learning for energy demand forecasting in the context of renewable energy integration
Module #19 Big Data and Distributed Computing for Energy Demand Forecasting Scalable machine learning for large-scale energy demand data using distributed computing
Module #20 Deep Learning for Energy Demand Forecasting Advanced deep learning techniques for energy demand forecasting
Module #21 Explainable AI for Energy Demand Forecasting Interpretable machine learning models for energy demand forecasting
Module #22 Energy Demand Forecasting for Peak Load Management Machine learning for energy demand forecasting in peak load management applications
Module #23 Energy Demand Forecasting for Smart Grids Machine learning for energy demand forecasting in smart grid applications
Module #24 Future Directions in Machine Learning for Energy Demand Forecasting Emerging trends and future directions in machine learning for energy demand forecasting
Module #25 Project Development and Presentation Develop and present a machine learning project for energy demand forecasting
Module #26 Capstone Project:Energy Demand Forecasting for a Real-World Scenario Apply machine learning techniques to a real-world energy demand forecasting problem
Module #27 Energy Demand Forecasting with External Factors Incorporating external factors such as weather and economic indicators into energy demand forecasting models
Module #28 Spatial and Temporal Energy Demand Forecasting Machine learning for spatial and temporal energy demand forecasting
Module #29 Energy Demand Forecasting for Electric Vehicles Machine learning for energy demand forecasting in electric vehicle charging applications
Module #30 Course Wrap-Up & Conclusion Planning next steps in Machine Learning for Energy Demand Forecasting career
