Lets start coding for linear regression

As from previous post, linear regression has analytical solution. For a given dataset the optimization problem reduces to linear algebra psudeo-inverse problem. 
``` W = (XX')^-1XY```
This would be the training step for the algorithm. Python code would be very easy. 

np.linalg.inv(X.dot(X.T)).dot(X).dot(Y)

Later once the parameters W is defined runtime would be a single matrix multiplication as our linear model.

Y' = WX

 Could be written as 

Y_est = W.dot(X)

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