Module 2: Supervised Learning - Regression & Regularization (Weeks 4)

This section focuses on advanced regression techniques backed by regularization and cross-validation to enhance model generalization in supervised learning.

Module Objectives (For Week 4)

This section outlines the module objectives for Week 4, focusing on understanding supervised learning with an emphasis on regression techniques and regularization methods.

Week 4: Regularization Techniques & Model Selection Basics

This section focuses on the importance of regularization techniques and model selection basics in supervised learning to enhance model performance and generalization.

Core Concepts

This section introduces critical concepts in supervised learning, focusing on understanding overfitting, underfitting, regularization techniques, and cross-validation.

Understanding Model Generalization: Overfitting And Underfitting

This section focuses on the concepts of overfitting and underfitting in machine learning models, explaining their characteristics, causes, and the importance of achieving generalization in modeling.

Regularization Techniques: L1 (Lasso), L2 (Ridge), Elastic Net

This section introduces regularization techniques, focusing on L1 (Lasso), L2 (Ridge), and Elastic Net methods to combat overfitting in regression models.

Introduction To Cross-Validation: K-Fold And Stratified K-Fold

This section introduces cross-validation techniques, focusing on K-Fold and Stratified K-Fold methods to assess model performance more reliably.

Lab: Applying And Comparing Regularization Techniques With Cross-Validation

This section explores the application of Ridge, Lasso, and Elastic Net regression models using cross-validation to improve predictive performance and generalization.

Lab Objectives

This section outlines the primary learning goals for Week 4, focusing on supervised learning techniques in regression and regularization.

Activities

This section provides practical activities to reinforce knowledge of regression techniques and cross-validation in machine learning.

Data Preparation And Initial Review

This section discusses the essential steps of data preparation and initial review necessary for implementing effective machine learning models, particularly focusing on regression techniques and regularization.

Initial Data Split For Final, Unbiased Evaluation (Crucial Step)

This section covers the crucial step of performing an initial data split before applying model training to ensure an unbiased evaluation of predictions.

Linear Regression Baseline (Without Regularization)

This section introduces the baseline linear regression model and its evaluation without any regularization techniques, emphasizing the importance of assessing overfitting and underfitting.

Implementing Ridge Regression With Cross-Validation

This section outlines the implementation of Ridge Regression alongside Cross-Validation techniques to enhance model generalization and prevent overfitting.

Implementing Lasso Regression With Cross-Validation

This section focuses on the implementation of Lasso regression using cross-validation techniques to improve model performance and avoid overfitting.

Implementing Elastic Net Regression With Cross-Validation

This section explores the implementation of Elastic Net regression with cross-validation to improve model performance and prevent overfitting.

Comprehensive Comparative Analysis And Discussion

This section addresses advanced regularization techniques in machine learning to combat overfitting while employing cross-validation for effective model evaluation.

Self-Reflection Questions For Students

This section provides self-reflection questions that encourage students to engage critically with the concepts of overfitting, underfitting, and regularization techniques in machine learning.