Unsupervised machine learning is a type of machine learning where algorithms analyze and interpret datasets without labeled outcomes or explicit instructions. Unlike supervised learning, which relies on predefined input-output pairs, unsupervised learning identifies hidden patterns, relationships, or structures within data. This approach is particularly useful for tasks like clustering, where similar data points are grouped, or dimensionality reduction, which simplifies complex datasets. For example, unsupervised learning can help identify customer segments based on purchasing behavior or detect anomalies in network traffic. It empowers systems to draw insights autonomously, making it a powerful tool for exploratory data analysis and uncovering insights in raw data.
