Description

Unsupervised Learning is a machine learning approach where algorithms are used to identify patterns and structures in data without the use of labeled examples. Unlike supervised learning, there are no predefined output categories or correct answers. Instead, the algorithm explores the data to find inherent groupings, relationships, or anomalies. This approach is particularly useful when dealing with large amounts of unlabeled data or when the goal is to discover hidden patterns or structures within the data.

Examples

• 👥 Customer segmentation
• 🚨 Anomaly detection
• 📚 Topic modeling in text analysis
• 📊 Dimensionality reduction

Applications