Scikit-learn in Depth – No Prep Lesson Pack – Unit 06

Scikit-learn in Depth – No Prep Lesson Pack – Unit 06

A complete, classroom-ready Scikit-learn teaching resource for beginner Computer Science, ICT, STEM, Python, artificial intelligence, machine learning and data science lessons.

This no-prep lesson pack introduces students to the professional Scikit-learn workflow, including estimators, datasets, preprocessing, pipelines, feature selection, cross-validation, GridSearchCV, model persistence and imbalanced datasets.

This resource is prepared as part of Fatih ARICA’s AI & Machine Learning Fundamentals 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 the role of Scikit-learn in Python machine learning projects
• Understand the estimator API and fit / predict workflow
• Work with built-in and generated datasets
• Recognise why preprocessing is needed
• Understand how pipelines make machine learning workflows safer and more repeatable
• Explain cross-validation and GridSearchCV
• Understand feature selection and model persistence
• Recognise the problem of imbalanced datasets
• Connect Scikit-learn workflows with responsible and reliable AI development

Ideal for Computer Science, ICT, STEM, beginner Python, AI, machine learning, Scikit-learn and data science lessons.

Series information:

This is Unit 06 of the AI & Machine Learning Fundamentals Series.

Previous units:
Unit 02 – AI Development Environment Setup
Unit 03 – Data Handling with NumPy & Pandas
Unit 04 – Data Visualisation with Matplotlib & Seaborn
Unit 05 – Machine Learning Basics

Continue with:
Unit 07 – Classification Algorithms

Resource type: Lesson (complete)
Age range: 14-16, 16+
Subject: Computer Science
Level: Beginner AI & Machine Learning