Course Structure#

Module 3: Supervised Learning - Regression and Classification (Weeks 5-6)#

  • Focus: Key concepts and algorithms in supervised learning.

  • Topics include regression, classification algorithms, decision trees, SVM, and ensemble methods.

Week 5: Supervised Learning - Regression#

  • Day 21: Introduction to Regression Analysis in Python

    • Basics of regression analysis and simple linear regression.

    • Math Focus: Linear equation fundamentals and fitting models to data.

  • Day 22: Implementing Multiple Linear Regression in Python

    • Understand and implement multiple linear regression.

    • Math Focus: Multivariate calculus and regression coefficients interpretation.

  • Day 23: Advanced Regression Techniques - Polynomial, Lasso, and Ridge Regression

    • Explore advanced regression techniques and their applications.

    • Math Focus: Polynomial functions, Lasso and Ridge regularization techniques.

  • Day 24: Regression Model Evaluation Metrics in Python

    • Key metrics for evaluating regression models.

    • Math Focus: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared.

  • Day 25: Addressing Overfitting and Underfitting in Regression Models

    • Strategies to combat overfitting and underfitting in regression.

    • Math Focus: Bias-variance tradeoff and regularization methods.