# Explain Normal Equation matrix formula

I’m just learning the Machine Learning course of Andrew Ng on coursera , and at the Normal Equation lesson I encountered a matrix formula to compute regression coefficients $\Theta$ without the explanation of how to come up with that.

There are some blogs on the Internet prove this formula in very detail and I just want to share an easy way to explain as well as to memorize the formula that is efficiently and handy.

So we have matrix $X$ as the design matrix, and $Y$ is the output vector of size (m+1). We want to find$\Theta$ matrix so that:

\$latex X \Theta = Y\$

All we want to do is isolating $\Theta$ in the left side (just like we always do to isolate x when solving a equation). And to do that, we want to “bring” what multiply by $\Theta$ to the right side, and we can do that only when “what” is a square matrix (make sense right ?).

So now $X$ is not a square matrix yet, we will multiply $X$ by its transpose matrix, which is \$latex X^{T}\$. Now we have:

\$latex X^{T}X\Theta = X^{T}Y\$

Now $X^{T}X$ is a square matrix, we can “bring” it to the right side:

$\Theta = (X^{T}X)^{-1}X^{T}Y$

And that’s it !