Machine Learning Regression and Prediction
Course Summary
This course provides a deep dive into regression techniques for machine learning, focusing on predictive modeling, feature selection, and performance optimization. Participants will explore various regression algorithms, including linear regression, polynomial regression, ridge regression, LASSO, decision tree regression, and advanced ensemble methods. The course emphasizes real-world applications, such as forecasting trends, risk assessment, and scientific data analysis, using Python and Scikit-learn.
Key Learning Outcomes
- Understand the fundamentals of regression analysis and its role in predictive modeling.
- Learn to evaluate model performance using metrics such as RMSE, R², and MAE.
- Apply decision tree regression and ensemble learning methods for improved predictions.
- Handle real-world regression tasks, including time-series forecasting and trend analysis.
