Here is the resource I used: Clustering with Scikit-Learn in Python by Programming Historian

The main things I learned/reviewed were:

• What k-clustering is (identifying groups with similar features to find patterns in data)
• Ways to decide how many clusters to choose (elbow method and silhouette score)
• How DBSCAN (Density-Based Spatial Clustering of Applications with Noise) works – good choice for data that is not linearly ordered and has dense clusters of data points