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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.
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