K-Means Clustering - Theory, Algorithm, Implementation, Scaling

This course teaches learners how to use K-Means clustering from theory to implementation. The learning outcomes include understanding the theory of machine learning, exploring the K-Means algorithm in a Colab Notebook, working with distance metrics, creating a K-Means pipeline, analyzing Elbow and Silhouette plots, and running K-Means simulations in serial and parallel. The course focuses on practical implementation, including spinning up a large cloud instance for massively parallel K-Means processing. The intended audience for this course is individuals interested in machine learning, data analysis, and implementing clustering algorithms.