This repository is intended for getting a good understanding of what Linear Regression is and how the cost function and gradient descent works under the hood. We all know how to use Linear Regression,
from sklearn.linear_model import LinearRegression
LinearRegression().fit(X, y)
However, that's not what we're gonna do here. I created this iPython notebook with a very simple example of only one input feature and one output value, for demonstrating how Univariate Linear Regression would work typically when you import and call such functions from libraries. This might help in a better understanding and therefore, more optimization and better results. Also, one more thing you can easily do is convert such models into APIs and use them as web services (will host a repository on this soon).
Meanwhile. to learn more about parameter learning and how the gradient descent algorithm works, visit this link. If you would like to learn more on the basics of Machine Learning, check out this repository. All thanks to Andrew Ng's amazing explanation on Coursera, if you haven't already taken the course, please go ahead and go through it soon.