This course offers a practical introduction to regression, a fundamental technique in supervised machine learning. You’ll explore the full workflow of building regression models from data splitting and feature selection to preventing overfitting through best practices.

Gain hands-on experience training models that predict continuous outcomes and evaluating performance using key metrics like MAE, MSE, and RMSE. Dive deep into linear regression and apply regularization methods such as Ridge, LASSO, and Elastic Net to enhance model accuracy and generalization.

By the end of the course, you’ll be able to distinguish regression from classification problems, implement and interpret linear models, use error metrics for model comparison, and choose the most effective regression approach for your data.

Perfect for aspiring data scientists and machine learning engineers, this course equips you with the technical and analytical skills needed to apply regression techniques to real-world problems.

Learning Objectives

• Build and test regression models to predict continuous numerical values.

• Assess model accuracy using performance metrics like MAE, MSE, and RMSE.

• Use regularization methods like Ridge, LASSO, and Elastic Net to reduce overfitting.

• Evaluate and compare models to choose the best fit for real-world tasks.

Course Syllabus

Reading: Course Prerequisites

Video: Welcome/Introduction Video

Module 1: Introduction to Supervised Machine Learning and Linear Regression

Reading: Learning Objectives
Video: Introduction to Supervised Machine Learning - Types of Machine Learning (Part 1)
Video: Introduction to Supervised Machine Learning - Types of Machine Learning (Part 2)
Video: Supervised Machine Learning (Part 1)
Video: Supervised Machine Learning (Part 2)
Video: Regression and Classification Examples
Practice Assignment: Practice Quiz: Introduction to Supervised Machine Learning
Video: Introduction to Linear Regression (Part 1)
Video: Introduction to Linear Regression (Part 2)
Video: (Optional) Linear Regression Demo – Part 1
Video: (Optional) Linear Regression Demo – Part 2
Video: (Optional) Linear Regression Demo – Part 3
App Item: Demo Lab: Linear Regression
App Item: Practice Lab: Linear Regression
Practice Assignment: Practice Quiz: Linear Regression
Reading: Summary/Review
Graded Assignment: Module 1 Graded Quiz: Introduction to Supervised Machine Learning and Linear Regression

Module 2: Data Splits and Polynomial Regression

Reading: Learning Objectives
Video: Training and Test Splits (Part 1)
Video: Training and Test Splits (Part 2)
App Item: Demo Lab: Training and Test Splits
Video: (Optional) Training and Test Splits Lab - Part 1
Video: (Optional) Training and Test Splits Lab - Part 2
Video: (Optional) Training and Test Splits Lab - Part 3
Video: (Optional) Training and Test Splits Lab - Part 4
Practice Assignment: Practice Quiz: Training and Test Splits
Video: Polynomial Regression
App Item: Practice Lab: Polynomial Regression
Practice Assignment: Practice Quiz: Polynomial Regression
Reading: Summary/Review
Graded Assignment: Module 2 Graded Quiz: Data Splits and Polynomial Regression

Module 3: Cross Validation

Reading: Learning Objectives
Reading: Important Note on Labs and Videos
Video: Cross Validation - Part 1
Ungraded Plugin: Reading: K-Fold Cross-Validation
Video: Cross Validation Demo - Part 1
Video: Cross Validation Demo - Part 2
Video: Cross Validation Demo - Part 3
Video: Cross Validation Demo - Part 4
Video: Cross Validation Demo - Part 5
App Item: Demo Lab: Cross Validation
App Item: Practice Lab: Cross Validation
Practice Assignment: Practice Quiz: Cross Validation
Reading: Summary/Review
Graded Assignment: Graded: Module 3 Quiz: Cross Validation

Module 4:

Reading: Learning Objectives
Video: Bias Variance Trade off (Part 1)
Video: Bias Variance Trade off (Part 2)
Video: Regularization and Model Selection
Video: Ridge Regression
Video: Lasso Regression (Part 1)
Video: Lasso Regression (Part 2)
Video: Elastic Net
Practice Assignment: Practice Quiz: Regularization Techniques
App Item: Demo Lab: Polynomial Features and Regularization
Video: Polynomial Features and Regularization Demo - Part 1
Video: Polynomial Features and Regularization Demo - Part 2
Video: Polynomial Features and Regularization Demo - Part 3
Practice Assignment: Practice Quiz: Polynomial Features and Regularization
Reading: Summary/Review
Graded Assignment: Module 4 Graded Quiz: Bias Variance Trade off and Regularization Techniques: Ridge, LASSO, and Elastic Net

Module 5: Regularization Details

Reading: Learning Objectives
Video: Further details of regularization - Part 1
Video: Further details of regularization - Part 2
App Item: Demo Lab: Details of Regularization
Video: (Optional) Details of Regularization - Part 1
Video: (Optional) Details of Regularization - Part 2
Video: (Optional) Details of Regularization - Part 3
App Item: Practice Lab: Regularization
Practice Assignment: Practice Quiz: Details of Regularization
Reading: Summary/Review
Graded Assignment: Module 5 Graded Quiz: Regularization Details

Module 6: Final Project

Reading: Project Scenario
Hands-on Lab: Final Project
Final Project Submission and Evaluation
Congratulations and Next Steps
Reading: Thanks from the Course Team

General Information

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Recommended Skills Prior to Taking this Course

To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.

Machine Learning: Regression

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