[MUSIC] Okay.
Well, we've talked quite extensively about using regression for the task of
predicting house value, but of course, the number of applications in which
regression can be used are quite large. Let's discuss just a few examples. So one is for predicting your salary after you take
this Machine Learning specialization. So you work really,
really hard in this specialization, and hopefully after that, you can go earn
a really cool job, have a very big salary. But to predict how much your salary will
be, maybe that depends on things like, what was your performance in the various
courses of the specialization? What was the quality of
your capstone project? How many forum responses did you make? And so on. Okay.
When we think about predicting your salary, y hat What we're gonna do is we're
gonna estimate our model parameters, w0, w1, w2 and w3, which are our intercept and
our weights on your performance in the class, the quality of your capstone,
and your participation in the forum. And when we're estimating these model
parameters, what we're gonna use, we're gonna use these features for other
students who have taken this course and the observations of what
their salaries were and the jobs they got after taking this
Machine Learning Specialization. Another application of regression is for
doing stock prediction. So here, we might think about
the fact that the prediction for the price of a stock tomorrow depends on
the recent history of the stock price. It depends on news events,
different things going on in the world, as well as the value of
other related commodities. And a very different application than
thinking about predicting stock prices or your salary after this specialization
is you're sitting on Twitter, you tweet something, and you wanna know how many people are gonna
eventually retweet what you just tweeted. Well, this might depend on how many
followers you have, how many followers your followers have, different features
of the text of what you tweeted, the popularity of
the hashtag you included, number of past retweets of your tweets
that you've had, and things like this. And it turns out these types of models
can actually be really good at predicting the eventual number of
retweets of your tweet. And another very different application
is maybe you have a smart house, there's lots of different
sensors in this house, and lots of different things you can control
like temperature settings in different locations, vents, window blinds,
and things like this. And you have a home office and you have
a desk, and you wanna be able to predict what's the temperature at your desk based
on various settings of all these controls. But there's actually no
sensor directly at your desk. So, what you can think about doing is fitting a spatial model in order
to predict what the temperature is at all these different
locations throughout the house. And this might depend on features such
as the thermostat settings in the house, whether the blinds are open or closed,
temperature outside, how you've set different vents, and as well as
things like the time of the day, okay? So, hopefully, you see that
applications of regression, and this really just touches the tip of the
iceberg of where regression is used, but it's really quite a powerful tool. [MUSIC]