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. [MUSIC]