🎯 What You'll Learn
- Explain core AI/ML paradigms
- Understand how models are trained
- Describe the ML lifecycle
- See where security risk enters
About This Workbook
You can’t secure what you don’t understand. This workbook covers the foundations of artificial intelligence and machine learning, framing the concepts that later AI-attack topics build on.
Chapter 1 — Learning Paradigms
The main ways machines learn.
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Chapter 2 — Models & Training
How a model goes from data to predictions.
- Data and features
- Training and evaluation
- Neural networks overview
Chapter 3 — The ML Lifecycle
Where risk enters from data to deployment.
- Pipelines
- Deployment
- Risk surfaces
Keep going. Work through each chapter in order, then apply what you learned in the matching labs.