Data Visualisation for AI & Machine Learning – Complete No-Prep Teaching Unit | Volume 1 Unit 04

AI & Machine Learning Volume 1 – Complete Curriculum Bundle | Course Book + 7 Teaching Units

AI & Machine Learning Volume 1 – Complete Curriculum Bundle combines the full 197-page course book with 7 complete teaching units, creating a structured pathway from the fundamentals of artificial intelligence to practical machine learning with Python. Designed for secondary, post-16, vocational and introductory computing learners, this bundle provides both the student-facing course content and the teacher resources needed to deliver it in the classroom. What’s included AI & Machine Learning: Foundations & Data – Volume 1 A complete 197-page European Edition course book covering Chapters 1–7, with explanations, Python examples, worked examples, exercises, European case studies and practical projects. 7 Complete Teaching Units: Unit 01 – What is Artificial Intelligence? AI foundations, branches of AI, real-world applications and responsible AI. Unit 02 – Setting Up Your AI Environment Python environments, Jupyter, Google Colab, development tools and practical setup. Unit 03 – NumPy & Pandas Data Handling Arrays, DataFrames, filtering, cleaning, grouping and preparing data for AI. Unit 04 – Data Visualisation Chart selection, interpretation, Matplotlib, Seaborn and evidence-based communication. Unit 05 – Introduction to Machine Learning Supervised and unsupervised learning, ML workflows, regression, evaluation and overfitting. Unit 06 – Scikit-learn in Depth Estimator API, pipelines, preprocessing, model selection, tuning and practical ML workflows. Unit 07 – Classification Algorithms K-NN, Decision Trees, Support Vector Machines, Random Forests, confusion matrices, precision, recall, F1 and responsible classifier selection. Each teaching unit follows a structured 4-lesson sequence, giving you 28 ready-to-teach lessons across Volume 1. Resources across the units include: • Teacher Guides • Detailed Lesson Plans • Teacher Handbooks • Student Summary Notes • Student Worksheets • Answer Keys • Activity Cards • Exit Tickets • Editable PowerPoint presentations • Supporting teacher and course materials The course progresses logically from understanding AI and preparing a Python environment through data handling and visualisation to complete machine learning workflows and classification. European examples and responsible AI considerations are integrated throughout, helping students connect technical skills with issues including bias, privacy, explainability, human oversight and the EU AI context. Ideal for • Computer Science and Computing courses • AI and Machine Learning introductions • Python and Data Science pathways • Post-16 and vocational computing • Teachers introducing AI into an existing curriculum • Independent study and enrichment programmes Volume 1 | Chapters 1–7 | 197-page Course Book | 7 Teaching Units | 28 Lessons A complete teaching and learning package for taking students from their first introduction to artificial intelligence through to building, evaluating and discussing practical machine learning systems.