- Introduction to Regression and Linear Regression
- This week provides an introduction to regression analysis as a powerful supervised learning method. You will delve into the concepts of linear regression, understanding its principles, assumptions, and practical applications.
- Polynomial Regression
- This week you will explore polynomial regression, an advanced technique used to capture nonlinear relationships between variables.
- Regularization
- This week focuses on regularization techniques, including Ridge, Lasso, and Elastic Net, which help prevent overfitting and improve the generalization of regression models.
- Evaluation and Cross Validation
- Throughout this week, you will explore evaluation metrics and cross-validation techniques to assess and optimize regression model performance.
- Ensemble Methods
- This week explores ensemble methods in regression analysis, including bagging and boosting, to combine multiple models for improved prediction accuracy.
- Case Study
- The final week focuses on a comprehensive case study where you will apply regression analysis to solve a real-world problem.
