Difference between (log, square, root) transformation and Normalization

I am confused between the Transformation and Normalization/Standardization, The basic understanding I have is Transformation: will be used in situation when we have skewness in data and to distribute the data to be like Gaussian. Normalization is used to rescale the data between 0 and 1 and make it Gaussian. what exactly does it differentiate the Transformation from Normalization?

• Your characterization of normalization is self-contradictory: one cannot reasonably aim to place data within a given range and make them approximately Gaussian. Although "normalization" is a generic term, in the majority of questions asked about it, the concept is merely a linear transformation to place the range of the data within a given interval such as $[0,1],$ without any consideration of how the data might be distributed within that interval. Search our site for (many) examples. "Standardization" is a linear transformation to center the distribution and make it of unit variance.
– whuber
Jan 24 '19 at 14:39