# How Machine Learning Works

> Master the core mechanics of machine learning, from data ingestion to model deployment, and understand where security vulnerabilities emerge.

Canonical: https://codelivly.com/rooms/how-machine-learning-works
Last updated: 2026-09-28

| | |
|---|---|
| Level | Beginner |
| Category | Cybersecurity |
| Lessons | 4 |
| Estimated time | 30 min |
| XP | 100 |

This module breaks down the fundamental mechanics of machine learning specifically for cybersecurity professionals. You will learn how ML differs from traditional software development, explore the three main training paradigms (Supervised, Unsupervised, and Reinforcement Learning), and map the end-to-end ML lifecycle. Crucially, you will learn to view each stage of this pipeline through a security lens, identifying the inherent vulnerabilities that adversaries exploit in data poisoning, model evasion, and AI supply chain attacks.

## Lessons

1. What is Machine Learning and Why It's Different from Traditional Programming (30 min)
2. Training vs. Inference Explained (30 min)
3. Types of Machine Learning (Supervised, Unsupervised, Reinforcement) (30 min)
4. Common ML Model Types Overview (30 min)

## Related

- [Learning path: AI and Machine Learning Fundamentals](https://codelivly.com/learning-paths/ai-and-machine-learning-fundamentals)
- [All rooms](https://codelivly.com/rooms)