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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.
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