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Normalization in machine learning is a preprocessing technique used to scale numerical data to a common range without distorting differences in the ranges of values. This is done to ensure that no single feature dominates the model's learning process due to its scale.

How Normalization Works in Machine Learning

Normalization is often applied to datasets in machine learning before feeding them into a model. Common methods include scaling all values to a range between 0 and 1, or transforming the data so that it has a mean of 0 and a standard deviation of 1. Normalization helps speed up the learning process and can lead to better performance, especially in algorithms sensitive to the scale of input data, like neural networks.

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