In standardization, the features will be rescaled with -

In standardization, the features will be rescaled so that they'll have the properties of a standard normal distribution with a mean of 0 and a standard deviation of 1.

Here are the steps:

1. Compute the mean and standard deviation of the feature.
2. Subtract the mean from each value of the feature. This is called centering the data.
3. Divide the centered data by the standard deviation. This is called scaling the data.

The result is a feature that has a mean of 0 and a standard deviation of 1. This process is important in many machine learning algorithms as they perform better or converge faster when features are on a relatively similar scale.