Regression Algorithms – No Prep Lesson Pack – Book 2 Unit 01

Regression Algorithms – No Prep Lesson Pack – Book 2 Unit 01

A complete, classroom-ready regression algorithms teaching resource for intermediate Computer Science, ICT, STEM, artificial intelligence, machine learning, Python and data science lessons.

This no-prep lesson pack introduces students to regression as a core supervised machine learning method for predicting continuous numerical values. Students explore linear regression, polynomial regression, residual analysis, regularisation with Ridge, Lasso and ElasticNet, and the correct use of logistic regression as a classification model.

This resource is prepared as part of Fatih ARICA’s AI & Machine Learning Intermediate teaching resource series and is designed to support the AI & Machine Learning: European Edition learning sequence.

What is included:

• Course Promo Page PDF and editable DOCX
• Full Teacher Package PDF and editable DOCX
• Lesson Plan PDF and editable DOCX
• Summary Notes PDF and editable DOCX
• Student Worksheet PDF and editable DOCX
• Answer Key PDF and editable DOCX
• Teacher Handbook PDF and editable DOCX
• Printable Activity Cards PDF and editable DOCX
• Exit Tickets PDF and editable DOCX
• PowerPoint slide deck
• 800 × 600 TES cover image
• Read Me First file

Students will learn to:

• Explain what regression means in machine learning
• Distinguish regression from classification
• Understand simple and multiple linear regression
• Interpret slope, intercept, coefficients and predictions
• Use residual plots to diagnose model problems
• Explain underfitting, good fit and overfitting
• Understand polynomial regression for non-linear relationships
• Compare Ridge, Lasso and ElasticNet regularisation
• Recognise why logistic regression is used for classification
• Connect regression models with responsible AI, bias, transparency and high-risk decision making

Ideal for:

• Computer Science lessons
• ICT and digital skills lessons
• STEM enrichment
• Artificial intelligence and machine learning units
• Python machine learning lessons
• Data science introduction lessons
• Intermediate AI / ML teaching
• Secondary, high school and vocational education
• Non-specialist teachers introducing regression models

Series information:

This is Book 2 Unit 01 of the AI & Machine Learning Intermediate Series.

Continue with:
Book 2 Unit 02 – Unsupervised Learning & Clustering
Book 2 Unit 03 – Model Evaluation & Validation
Book 2 Unit 04 – Neural Networks from Scratch
Book 2 Unit 05 – Deep Learning with TensorFlow & Keras
Book 2 Unit 06 – Computer Vision
Book 2 Unit 07 – Natural Language Processing

This resource can be used as a standalone complete lesson pack or as part of the full intermediate AI and Machine Learning teaching sequence.

Resource details:

Resource type: Lesson (complete)
Age range: 14-16, 16+
Subject: Computer Science
Level: Intermediate AI & Machine Learning
Format: PDF, editable DOCX and PowerPoint PPTX files