[MUSIC] So that's multiple regression. And as you've seen, we've talked about
what we said was the most widely used machine learning tool out there,
along with the most widely used algorithmic tool,
this gradient descent algorithm. So this was a really, really important
module that we've just covered. And now that you've worked through
this module, what you're able to do is describe linear regression when you have
multiple features of just a single input, when you're talking about
polynomial regression. Or things like modeling seasonality and
time series, but we also talked about how to handle multiple different inputs,
and features of these different inputs. And all of these models fall in
the context of multiple regression. And for this multiple linear regression
model we talked about how to fit the model, using both a closed form
solution as well as gradient descent. And we also talked about how we can
interpret the coefficients of some cases of this multiple regression model
as well as using it for prediction. [MUSIC]