Machine Learning Workflows in Python from Scratch Part 2: k-means Clustering

In the first part of this series, we started off rather slowly but deliberately. The previous post laid out our goals, and started off with some basic building blocks for our machine learning workflows and pipelines we will eventually get to. If you have not yet read the first installment in this series, I suggest that you do so before moving on. This time around we pick up steam, and will be doing so with an implementation of the k-means clustering algorithm. We will discuss specific aspects of k-means as they come up while coding, but if you are interested in a superficial overview of what the algorithm is about, as well as how it relates to other clustering methods, you could check this out.