So in this module we're gonna talk about regression.
Which is one of the most widely used statistical tools outs there.
And the idea is really simple.
So we have some set of features and we wanna model how our observations that
are associated with these features change as we change the values of the features.
And we're gonna ground our conversation in a case study
of trying to predict house values.
So here we can imagine that a house has some set of features like what's the size
of the house, how many bedrooms does it have, number of bathrooms and
the list goes on and on.
And the observation that we have is what's the value of the house or
the house sales price.
But the tools of regression go
much beyond just thinking about doing prediction tasks.
So as we're gonna see in the classification course,
we can use regression tools for classification.
So for example, let's say we have an email and
we wanna classify whether it's spam or not spam.
Well, that email we can think of having features about the text of the e-mail,
that's indicative of whether that e-mail is spam or not spam.
And in addition we can think about using regression for
analyze the importance of the features themselves.
And we're gonna talk more about that in the regression course.
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