Hey there! Today, we're going to learn about feature scaling. You might be wondering, what is feature scaling, and why should we care? Simply put, feature scaling is like making sure all the ingredients in your recipe are measured in the same unit. Imagine trying to mix pounds of flour and teaspoons of salt without converting one to the other — it wouldn't make sense, right?

Our goal is to understand why feature scaling is crucial in machine learning and to learn how to do it using Python and a library called SciKit Learn.

Feature scaling ensures that all your data features contribute equally when building a machine learning model. Without scaling, large values might dominate, leading to biased outcomes. For example, if predicting house prices, and one feature was in thousands (like square footage) and another in single digits (like the number of rooms), the model might overlook the smaller values just because they seem less relevant.

There are two common types:

1. Standardization: Transforms data to have a mean ($\mu$) of 0 and a standard deviation ($\sigma$) of 1.