Unsupervised Learning: Finding Hidden Patterns in Data

Unsupervised learning differs from supervised learning in that it deals with unlabeled data. Instead of learning from predefined labels, the algorithm identifies patterns, structures, and relationships within the dataset.

Key unsupervised learning algorithms include:

• K-Means Clustering: A method for grouping similar data points into clusters based on their features.
• Hierarchical Clustering: Creates a tree-like structure to represent nested clusters in data.
• Principal Component Analysis (PCA): Reduces dimensionality and helps visualize complex datasets by extracting essential features.
• Autoencoders: A type of neural network used for anomaly detection and feature extraction.

Unsupervised learning is essential for tasks like customer segmentation, anomaly detection, and recommendation systems.