[MINI] k-means clustering

The podcast discusses the k-means clustering algorithm and its objective of grouping data points into clusters without guidance. It explores tracking animal movements and customer segmentation using k-means clustering. The concept of clusters and centroids is explained, along with classifying new data points. The chapter covers accuracy, precision, and trade-offs in k-means clustering. Lastly, it explores clusters, head positioning, data visualization, and the application of k-means clustering in the workplace.