Introduction to Unsupervised Learning

Unsupervised learning is a fundamental branch of ML that deals with the training of models on unlabeled data. It encompasses a wide range of algorithms and techniques that aim to identify groups or patterns in the data based on input features. Unlike supervised learning, which relies on labeled data, unsupervised learning aims to extract meaningful information from input features without any predefined outcomes. By exploring the inherent structure of the data, unsupervised learning enables the identification of hidden patterns and the generation of valuable insights.

One real-life application of unsupervised learning that is challenging to frame in terms of supervised learning is anomaly detection in cybersecurity, where unsupervised learning techniques are used to identify unusual or anomalous patterns within large datasets of network traffic, system logs, or user behavior. The challenge here is that, in many cases, it’s difficult to have labeled data that explicitly indicates which patterns are normal and which are malicious.