My take on the first exercise of Andrew Ng's Machine Learning course on Coursera.
The notebook is divided in 3 parts.
In the first part, I implement univariate linear regression from scratch. I also visualize gradient descent and the path it took to minimize the cost function.
Here I implement multivariate linear regression from scratch and make a prediction.
I implement linear regression in a more practical way using scikit-learn. I use dataframes and leverage the power of this tool to avoid writing an algorithm from scratch.