Feature scaling via data normalization is a popular method of input normalization in Machine Learning. It provides several benefits to the learning process. In fact, it improves the learning speed when a local optimization algorithm is used. This technique mainly consists of first mean-centering and the resclaing each of the input features by the inverse of its standard deviation. This simple technique can significantly improve the data analysis and provide better modeling insights.