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Last updated

27 July 2026

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AI Development Environment Setup – Complete Teacher Resource Pack is Unit 02 of the AI & Machine Learning Fundamentals Series.

This complete no-prep teaching pack helps students understand the development environment behind beginner Artificial Intelligence and Machine Learning work. Students learn that AI projects are not only about writing code. They also depend on the correct Python environment, installed packages, notebook kernels, tool choice and a reliable workflow.

The unit introduces learners to the AI development stack, including Python, Anaconda, virtual environments, pip, conda, Jupyter Notebook, JupyterLab, Google Colab, Kaggle and VS Code.

Students explore how these tools fit together and learn how to diagnose common beginner setup problems such as missing packages, wrong notebook kernels, package conflicts, blocked installations and interpreter confusion.

This resource is designed for secondary computing, high school computer science, digital literacy, STEM, introductory AI and beginner machine learning lessons.

What students will learn:

By the end of this unit, students will be able to:

  • explain why development environments matter in AI projects
  • identify the main layers of an AI development stack
  • describe the roles of Python, Anaconda, pip, conda and virtual environments
  • explain Jupyter notebooks, kernels, cells and run order
  • compare local tools, cloud notebooks and VS Code
  • choose suitable tools for different classroom or project scenarios
  • diagnose common beginner setup problems
  • prepare for data handling work with NumPy and Pandas in the next unit

Lesson structure:

This unit is designed for 4 × 40-minute lessons:

  1. The AI Development Stack
  2. Anaconda, Virtual Environments and Package Management
  3. Jupyter Notebook and JupyterLab Workflow
  4. Cloud and Professional Tools: Colab, Kaggle and VS Code

Included files:

  • Course Promo Page
  • Teacher Guide
  • Teacher Handbook
  • Student Summary Notes
  • Student Worksheet
  • Answer Key
  • Printable Activity Cards
  • Printable Exit Tickets
  • Editable PowerPoint Slides
  • Editable Word Files
  • Read Me First guide
  • TES cover image

Ideal for:

  • Artificial Intelligence lessons
  • Machine Learning introduction units
  • Python setup lessons
  • Computer Science classes
  • STEM lessons
  • Digital literacy programmes
  • Data science preparation
  • No-prep cover or extension lessons
  • Teachers introducing AI tools before practical coding

Teacher benefits:

This pack helps teachers:

  • avoid losing class time to uncontrolled setup problems
  • explain AI development tools clearly
  • teach local and cloud setup routes
  • support students using different devices
  • introduce troubleshooting without overwhelming beginners
  • prepare students for practical data work in later units
  • assess readiness before moving to NumPy and Pandas

Core unit message:

A reliable AI project begins with a reliable development environment.

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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