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So that's multiple regression.

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And as you've seen, we've talked about
what we said was the most widely used

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machine learning tool out there,
along with the most widely used

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algorithmic tool,
this gradient descent algorithm.

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So this was a really, really important
module that we've just covered.

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And now that you've worked through
this module, what you're able to do is

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describe linear regression when you have
multiple features of just a single input,

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when you're talking about
polynomial regression.

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Or things like modeling seasonality and
time series, but we also talked about how

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to handle multiple different inputs,
and features of these different inputs.

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And all of these models fall in
the context of multiple regression.

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And for this multiple linear regression
model we talked about how to fit

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the model, using both a closed form
solution as well as gradient descent.

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And we also talked about how we can
interpret the coefficients of some cases

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of this multiple regression model
as well as using it for prediction.

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