[MUSIC] Okay so in the first course
of this specialization, we introduced this block diagram or
flow chart for regression. As well as a bunch of other
machine learning tasks, but I just want to walk through this again. So we're going to assume that
we have some training data. Okay, so all of a sudden I've
said the word training data, and I hadn't said that to this point. That's a topic we discussed
at a very high level in the first course of the specialization, and we're gonna discuss the concepts of
training data, test data, validation sets, lots of other ideas about fitting
our models and accessing our fits. And chosing between models and
all these different topics. We're gonna discuss that in this course. But for right now, if you don't know what
it means to have training data, go back to the first course of this specialization,
watch that video or look it up online. For now, I'm gonna assume that you
know that we've some training data and we're using our training data
to fit a specific model. And for the rest of this module,
that's gonna suffice, we're only looking at training data. And all the other discussion
about validation sets, test sets, and everything like this,
choosing between models, assessing the fit of the models,
that's going to come later in this course. Okay, so when I talk about data
in the rest of this module, I'm assuming we're talking
about just our training set, that we've already done some split and
we have our training data, okay. Think I've said enough on that. So, we have our training data and
in this case, that's gonna be some table of house id, house square feet and
then the house sales price. And we have this big table
of all these quantities. And then what we're going to do,
is extract some features. So maybe, actually, at this point instead
of specifically saying how square feet, I can just say that there's
some set of house attributes. We might have things in
addition to square feet. And so one step we're gonna have to do is
figure out what input we're gonna use for our regression model. We're gonna talk about more general
features later in the course, but for now we're just assuming a simple setup
where we're gonna choose one of our house attributes, call that the input to this
regression model, and work from there. So, we've talked about using
square feet as our selected input and then we're gonna use
our Machine Learning model, which is regression,
to predict our house sales prices. So y hat represents our predicted, house sales price. And how are we predicting it? Well, based on some estimated line or
curve, so this is our,
f hat is our estimated function. That's fit from our data set, or,
specifically our training data set. And how are we gonna determine f hat? Or how are we gonna
estimate this function? Well, first we're gonna need to
describe some quality metric that says I should describe what Y is. I already mentioned this earlier,
but let's write it down explicitly. This is the sales price, the actual sales price of our houses, and
we're gonna compare the actual sales price to the predicted sales
price using the any given F hat. We're gonna say how well did we do? And that's what the quality metric is. So there's gonna be some error
in our predicted values. And the machine learning algorithm
we're gonna use to fit these regression models is gonna
try to minimize that error. So it's gonna search over all these
functions to reduce the error in these predicted values. Okay, so
this is our overall flow chart and we're gonna walk through the various
components of this throughout this module and
in more depth in the rest of this course. [MUSIC]