1Introduction
9Preparing Data: Cleaning, Normalization, and Feature Engineering
2What is Machine Learning? An Introduction
10Training and Testing: How to Build a Model
3The Difference Between AI, Machine Learning, and Deep Learning
11Overfitting, Underfitting, and Model Evaluation
4Types of Machine Learning: Supervised, Unsupervised & Reinforcement Learning
12Real-World Applications of Machine Learning
5Key Concepts: Data, Features, Labels, and Models
13Ethical Considerations: Bias, Fairness, and Privacy
