Unsupervised learning is like exploring a new city without a map. We're trying to find patterns and structures in the data without any predefined labels.
Clustering is like grouping similar items together. K-means clustering forms clusters by calculating distances between data points.
Imagine capturing a 3D world in a 2D painting. Dimensionality reduction techniques like PCA help us represent complex data in simpler forms.