/Stanford-ML-ex1

My take on the first exercise of Andrew Ng's Machine Learning course on Coursera.

Primary LanguageJupyter Notebook

Stanford-ML-ex1

My take on the first exercise of Andrew Ng's Machine Learning course on Coursera.

The notebook is divided in 3 parts.

Part 1

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.

Part 2

Here I implement multivariate linear regression from scratch and make a prediction.

Part 3

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.