Feature Scaling and Mean Normalization In my machine learning class these two methods were discussed and mentioned that both should be used. I have a couple questions about this:

1) Can I mix and match these two approaches? e.g. Feature Scale x1 and Mean Normalize x2?

2) How do you determine which of these options to apply? It seems that either could accomplish the task of increasing your convergence rates... I suppose you just need to know your data set to understand which will reliably reduce your values while leaving as few outliers as possible?
Mean normalization is a form of feature scaling, so these are not really two different approaches. Feature scaling is just a more general term. What kind of feature scaling, e.g. mean normalization,  you need to use ultimately depends on the data.