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**Ordinary Least Squares (OLS)**
→ Supervised Learning
- Derivation of OLS

→ Fit a line of the form y = mx + c or y = b0 + b1x
→ Concept of actual y (𝐲i) and estimated y (ỷi)
→ Minimize the squared deviation between actual and estimate.

**The Derivation :-**

**Derivation :-**
- Our goal is to minimize SSE:

SSE = ∑ (yi - b0 - b1xi)2
- We use basic ideas from calculus: Take the first derivative and equate it to 0.

**Derivation for b0**
**Derivation of b1**

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