This project is part of my Machine Learning journey.
It explains Supervised Learning with examples, quiz answers, and assignments.

• "Supervised" means a teacher is present.
• We provide the computer with:
  • Inputs (X) → examples
  • Outputs (Y) → correct answers
• The machine learns a mapping rule:
  Input (X) → Model → Output (Y)

1. Regression → Predict numbers (continuous values).
   Example: Predicting fuel price, predicting house prices.

2. Classification → Predict categories (labels).
   Example: Pass/Fail, Spam/Not Spam.

1. In supervised learning, what do we provide?
   ✅ Inputs + outputs

2. Predicting whether an email is spam or not is:
   ✅ Classification

3. Predicting tomorrow’s temperature is:
   ✅ Regression

In the next lesson, I will:

• Train my first real ML model (Iris dataset).
• Upload code and explanations.