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Free Machine Learning Courses to kick start your learning
Level- Beginners:
- Supervised Learning
- UnSupervised Learning
- Advance Learning
ML Engineer Responsibilities:
- Data collection
- Preprocessing
- Model selection and training
- Performance evaluation
- Continuous model improvement.
Normalization vs Standardization: Why Theyβre Not the Same
People treat these two as interchangeable. theyβre not.
π Normalization (Min-Max scaling):
Compresses values to 0β1.
Useful when magnitude matters (pixel values, distances).
π Standardization (Z-score):
Centers data around mean=0, std=1.
Useful when distribution shape matters (linear/logistic regression, PCA).
π Key idea:
Normalization preserves relative proportions.
Standardization preserves statistical structure.
Pick the wrong one, and your modelβs geometry becomes distorted.
