In the course 1 we have only dealt with multiple linear regression of the form w1x1+w2x2…+b
In course 2 we have dealt to fit the neural network with polynomial regression of form like
w1x1+w2x2^2…

Could you please explain why is this distinction and use cases? And also uses cases of linear and non linear models and what are they exactly

How do I decide upon to use a multiple or polynomial regression model which brings me to point of choosing a linear and non linear models. So, I want to understand linear and non linear models and how they affect in choosing the regression model between a neural network and machine learning model

The way I look at it, there really aren’t any non-linear models. There are just linear models that are constructed using additional features from non-linear processes (like polynomial terms).

The way you decide is by iterating on the model design, adding just enough complexity to give good-enough results.