[MUSIC]
Now let's delve into what this course is gonna be about.
In the Foundations course,
we talked about how machine learning is about deriving intelligence from data.
And in this course,
the machine learning method that we're gonna focus on is regression.
And in particular, what regression is gonna assume is that we have some features
that are derived from our data that are the input to our regression model.
And then our goal will be to predict some continuous valued output or
response to the input.
The way we're gonna do this is by learning a relationship between our inputs x and
this output y.
For example, maybe you're interested in how taking this machine learning
specialization is going to pay off for you in the end.
So you're sitting here, you're doing a lot of really hard work and
you wonder where this is going to land you.
Well, maybe a question you might be interested in is what will your salary be
after taking this specialization.
And so we can think about predicting what your salary is
based on things like what your performance was in the various courses,
the quality of your capstone project, how many
forum responses you are participating in, and different features like this.
So this would be the input to the regression model and
the prediction, the output that we're trying to predict would be
your expected salary at the end of this specialization.
Another example is predicting the price of a stock.
And to form this prediction maybe we expect that this would
depend on the past history of the stock, as well as perhaps recent news events,
in addition to the trends in other related commodities.
Or maybe you tweeted something, and
you wanna know how many people are gonna retweet what you tweeted.
Well this might depend on how many followers you have,
how many followers your followers have,
local structure of your follower network, what hash tags you used,
how many retweets you've had in the past, and other features like this.
Another example that we're going to talk about in this course is
a really cool example of reading your mind.
Where you go and you get some kind of brain scan, could be FMRI or
MEG and for our sake we're just going to think of it as producing an image of your
brain even though the truth is it produces something more complicated.
But we can think of all the different pixel intensities
as inputs to a regression model where the goal of the output is to
predict whether you felt happy or sad in response to something you were shown
when you were getting that brain scan.
So it's reading your mind because we want to guess how you're feeling just from
an image of your brain.
But in this course,
we're gonna focus in on a case study of predicting house prices.
So in particular, a question we're gonna ask is, what's the value of a given house?
Maybe you wanna sell your house and
you wanna figure out how much to list that house for.
And so we're gonna derive this intelligence by looking at some data.
And the data we're gonna look at include other house sales.
So we're gonna have the sales price associated with a bunch of other houses,
as well as the house attributes of these other houses, and
from these inputs, the house attributes, we're gonna learn this relationship
between house attributes and the output, which is the sales price, and
use this learned model in order to make the prediction of the value of your house.
And this course is all about how to form
this relationship between the input and the output.
[MUSIC]