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27 July 2026

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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

Get this resource as part of a bundle and save up to 33%

A bundle is a package of resources grouped together to teach a particular topic, or a series of lessons, in one place.

Bundle

AI & Machine Learning Complete No-Prep Bundle | Volume 1: Foundations & Data

Teach Artificial Intelligence, Python, data science and introductory Machine Learning with one complete, ready-to-use bundle. This collection brings together the full set of AI & Machine Learning No-Prep Lessons created for Volume 1: Foundations & Data, alongside the complete 192-page course book. The resources follow a clear learning journey from the foundations of Artificial Intelligence to data handling, visualisation and the development of students’ first Machine Learning models. Each lesson is designed to reduce preparation time while providing structured explanations, practical activities and classroom-ready materials. The bundle covers all seven units: • Introduction to Artificial Intelligence • AI history, branches and real-world applications • Artificial Intelligence in Europe and the EU AI Act • Python, Anaconda and development environments • Jupyter Notebook, Google Colab, Kaggle and VS Code • NumPy and Pandas for data handling • Missing data and real European datasets • Data visualisation with Matplotlib and Seaborn • Supervised, unsupervised and reinforcement learning • The Machine Learning workflow • Overfitting, underfitting and evaluation metrics • Linear regression and feature engineering • Scikit-learn workflows and model development • Hyperparameter tuning and ensemble methods • K-Nearest Neighbours, Decision Trees, Support Vector Machines and Random Forests The included No-Prep Lessons provide ready-to-teach classroom support through presentations, student worksheets, practical activities, answer materials, assessment opportunities and lesson review tasks. The full course book adds detailed explanations, diagrams, code examples, comparison tables, key concepts, worked examples and practice exercises. Students also complete practical tasks using Python, Jupyter, NumPy, Pandas, Matplotlib and Scikit-learn. This bundle is suitable for secondary Computer Science, vocational education, introductory AI courses, coding clubs, homeschooling and independent learning. No previous Artificial Intelligence or Machine Learning experience is required. All resources in this bundle are also available separately. Purchasing the complete bundle provides the full Volume 1 learning sequence in one organised collection and offers better value than purchasing each No-Prep Lesson individually. What’s Included This bundle includes: Full 192-page AI & Machine Learning: Volume 1 — Foundations & Data course book Complete collection of Volume 1 No-Prep Lesson packs Teaching presentations Student worksheets Practical coding and data activities Answer materials Review and assessment tasks Classroom activities and exit-ticket style checks Resources covering all seven chapters of Book 1

£15.00

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