AI & Machine Learning Foundations
Master Decision Trees, Supervised & Unsupervised Learning, KNN, Validation Splits, Overfitting prevention, AI Fairness, and Ethics in Class 7!
Course Chapters & Learning Modules
Total 10 interactive modules with guided checkpoint challenges.
Module 1 — Decision Trees
Learn how decision trees make predictions by asking sequential questions through nodes, branches, and leaves.
Module 2 — Supervised Learning
Understand supervised learning with labelled datasets, input features, target labels, and generalisation.
Module 3 — Unsupervised Learning
Explore discovering hidden clusters and anomalies in unlabelled datasets without pre-assigned answers.
Module 4 — KNN Classification
Master K-Nearest Neighbours, distance calculations, choosing K, feature scaling, and majority voting.
Module 5 — Training vs Validation
Learn how dataset splits (train, validation, test) enable fair model selection and prevent evaluation leakage.
Module 6 — Overfitting
Understand overfitting and underfitting, recognize signs of memorisation vs generalisation, and apply corrective strategies.
Module 7 — AI Fairness
Understand fairness in AI systems, identify sources of bias and proxy features, and audit group error disparities.
Module 8 — Ethics in AI
Explore AI ethics, privacy safeguards, data minimisation, informed consent, transparency, and accountability.
Module 9 — Smart Prediction Project
Build an end-to-end responsible prediction project: problem formulation, feature engineering, validation, and error reporting.
Module 10 — Combined Revision & Test
Synthesize all Class 7 AI & ML concepts, master debugging workflows, and complete the 10-Question Checkpoint Quiz.
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