Data

• Training, validation and test data, and why the split matters
• Features, labels and the origin of bias in a dataset
• Overfitting explained without mathematics, and how it shows in practice

Methods

• Classification, regression and clustering with worked everyday examples
• Neural networks and what "deep" actually refers to
• Language models: next-token prediction, context and hallucination

Practice

• Prompting patterns that improve reliability, and verification habits
• Recognising confident-sounding errors in generated output
• Where a human must remain accountable for the result

Responsibility

• Privacy, consent and what happens to data entered into a tool
• Bias, fairness and representational harm in accessible terms
• Building a personal learning path toward a specialist track