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Okay, so in this module we've talked about simple linear regression.
And what we've seen is we've described what the model is.
We just have a single input, single output, and fitting a line or model.
It's just a simple line, to describe the relationship between our input x and
our output y.
We've talked about goodness of fit of a specific line to our data and the measure
being the residual sum of squares that we've talked about in this module.
And we've also talked about some ways to think about
interpreting our fitted line and using it to form predictions.
But a big emphasis was on thinking about how do we actually fit
that line to the data, and we talked about different optimization techniques.
The big one being, gradient descent, and using that to minimize our residual
sum squares, to come up with our fitted line that we're gonna use for predictions.
Even though this is a very very simple and
basic tool, it's actually incredibly powerful.
And we'll look at this in some of our assignments.
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