<a target="_blank" href="https://www.almabetter.com/bytes/tutorials/data-science/normalization-in-machine-learning">Data normalization in machine learning</a> is a preprocessing step to rescale features to a standard range, typically [0, 1] or [-1, 1]. This ensures that no single feature dominates the model due to its scale. Normalization improves model performance and convergence speed in algorithms like gradient descent.
Common normalization techniques:
1. Min-Max Scaling: Scales features to a fixed range, usually [0, 1].
2. Z-score Standardization: Centers data around the mean with a unit standard deviation.
3. Max Abs Scaler: Scales each feature by its maximum absolute value.
Normalization is crucial for algorithms sensitive to feature scales, such as k-nearest neighbors and neural networks, enhancing accuracy and computational efficiency.

[Data normalization in machine learning](https://www.almabetter.com/bytes/tutorials/data-science/normalization-in-machine-learning) is a preprocessing step to rescale features to a standard range, typically \[0, 1\] or \[-1, 1\]. This ensures that no single feature dominates the model due to its scale. Normalization improves model performance and convergence speed in algorithms like gradient descent.

**Common normalization techniques:**

**1\. Min-Max Scaling:** Scales features to a fixed range, usually \[0, 1\].

**2\. Z-score Standardization:** Centers data around the mean with a unit standard deviation.

**3\. Max Abs Scaler:** Scales each feature by its maximum absolute value.

Normalization is crucial for algorithms sensitive to feature scales, such as k-nearest neighbors and neural networks, enhancing accuracy and computational efficiency.