Feature
scaling is an integral part of preprocessing our dataset before we
delve into the machine learning model and mind this, it can create a
world of difference.
It
speeds up gradient descent by making it require fewer iterations to
get to a good solution. Feature scaling speeds
up gradient descent by avoiding many extra iterations that are
required when one or more features take
on much larger values than the rest.
This is the main reason for using feature scaling.