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Normalization is a data preprocessing technique that can be used to scale the input features to a common range, such as [-1, 1] or [0, 1]. Normalization can help reduce the effect of outliers, improve the convergence of gradient-based algorithms, and prevent variables with a larger magnitude from dominating the model. One common method of normalization is standardization, which transforms each feature to have a mean of 0 and a variance of 1. This can be done by subtracting the mean and dividing by the standard deviation of each feature. Standardization can be useful for models that assume the input features are normally distributed, such as linear regression, logistic regression, and support vector machines. References:

Data normalization and standardization: A video that explains the concept and benefits of data normalization and standardization.

Standardize or Normalize?: A blog post that compares different methods of scaling the input features.