Machine Learning & Artificial Intelligence | Data Science Free Courses

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Normalization vs Standardization: Why They’re Not the Same People treat these two as interchangeable. they’re not. 👉 Normalization (Min-Max scaling): Compresses values to 0–1. Useful when magnitude matters (pixel values, distances). 👉 Standardization (Z-score): Centers data around mean=0, std=1. Useful when distribution shape matters (linear/logistic regression, PCA). 🔑 Key idea: Normalization preserves relative proportions. Standardization preserves statistical structure. Pick the wrong one, and your model’s geometry becomes distorted.

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