Unsupervised learning is a strong tool in machine learning. It helps us do things like grouping data and finding unusual patterns.

Clustering

1. What is Clustering?: Clustering is when we group similar items based on their characteristics.
2. Example: Think about sorting customers by what they buy. Using methods like K-means or hierarchical clustering, businesses can find different groups of customers without needing labels.

Anomaly Detection

1. What is Anomaly Detection?: Anomaly detection looks for rare items or facts that stand out from most of the data.
2. Example: In checking for fraud, unsupervised learning can spot strange transaction patterns. This helps companies act quickly.

By using methods like reducing the number of dimensions in data (like PCA) and clustering, unsupervised learning uncovers hidden patterns in data. This makes it extremely useful for exploring and understanding data!