While normalization focuses on scaling data to a specific range, standardization aims to transform data to have a mean of zero and a standard deviation of one. This article could delve deeper into various standardization techniques beyond Z-score normalization, exploring methods like MaxAbsScaler (which scales data to the [-1, 1] range based on the maximum absolute value), PowerTransformer (which applies a power transform to make data more Gaussian-like), and QuantileTransformer (which transforms data to follow a uniform or normal distribution based on quantiles). It would elaborate on the mathematical underpinnings of each technique, providing Python code examples using scikit-learn. Furthermore, it would offer guidance on selecting the appropriate standardization method based on data characteristics and the specific machine learning algorithm being used.