The video tutorial explains the importance of feature scaling in data processing, particularly in deep learning. It introduces the concept of standardizing independent features to a fixed range to improve calculation efficiency. The tutorial demonstrates how to implement feature scaling using the Standard Scaler class from the sklearn library in Python. It highlights the difference between scaling training and test data, emphasizing the use of fit_transform for training data and transform for test data. Finally, it verifies the scaled data using Jupyter Notebook.

What are the potential issues if feature scaling is not performed?

Describe the steps to perform feature scaling using the standard scaler in Python.