This is a very quick section: I just want to remind you about the importance of normalizing your data, making sure that your various input feature data is on the same scale, and is comparable. And, sometimes it matters, and sometimes it doesn't. But, you just have to be cognizant of when it does. Just keep that in the back of your head because sometimes it will affect the quality of your results if you don't.
So, sometimes models will be based on several different numerical attributes. If you remember multivariant models, we might have different attributes of a car that we're looking at, and they might not be directly comparable measurements. Or, for example, if we're looking at relationships between ages and incomes, ages might range from 0 to 100, but incomes in dollars might range from 0 to billions, and depending on the currency...
