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

Explore k-means clustering from theory to implementation in this comprehensive 26-minute video tutorial. Dive into the theory of machine learning, examine a Colab notebook demonstrating the K-Means algorithm, and learn about distance metrics. Create a K-Means pipeline, interpret elbow and silhouette plots, and run K-Means simulations in both serial and parallel modes. Witness the power of cloud computing by spinning up a massive Cloud9 instance with 128 GB RAM and 32 vCPUs to execute large-scale parallel K-Means clustering. Gain practical insights into scaling machine learning algorithms for real-world applications.

Syllabus

Intro
Theory of Machine Learning
Colab Notebook exploration of K-Means Algorithm
Distance Metrics
Creating K-Means Pipeline
Elbow Plots
Silhouette Plots
Running K-Means Serial Simulation
Running K-Means Parallel Simulation
Spinning up Huge Cloud9 128 GB Ram 32 vCPU Instance
Running massively parallel K-Means