Hello, in Week 2 Principal Component Analysis, PCA Algorithm lecture, it has been told that 2 preprocessing should be done before applying the algorithm:

- Make your data to have zero mean,
- Make your features to have similar range.

The second, I do understand as PCA works (kind of) by maximizing variance, which requires calculation of Euclidean Distance. But the first one, I can’t really understand why we need to force the data to have zero mean. Does it make any difference?

Thanks!