Introduction to Machine Learning

Welcome to the final phase of our journey: **Phase 4—Machine Learning Foundations**. Today is **Day 141**.

What is Machine Learning (ML)?

In traditional programming, you write the Rules: `if x > 10 then do Y`.

In Machine Learning, you give the computer the **Data** and the **Answers**, and it figures out the **Rules** itself.

1. **Supervised Learning**: You have "Labeled" data (e.g., Photos labeled "Cat" or "Dog"). The goal is to predict the label for new data.

2. **Unsupervised Learning**: You have no labels. The computer finds patterns on its own (e.g., "These 500 customers have similar buying habits").

3. **Reinforcement Learning**: The computer learns through trial and error (e.g., A robot learning to walk).

In Python, the king of ML is **Scikit-Learn**. It's built on NumPy and Matplotlib, making it the perfect final step for our stack.

Research "The difference between AI, Machine Learning, and Deep Learning." (Hint: One is a subset of the other).

*Day 142: Regression vs Classification.*