Unsupervised Learning is a type of machine learning that deals with data without labeled responses. It aims to identify patterns and structures within the data, allowing algorithms to learn from the input data alone. Common techniques include clustering, where data points are grouped based on similarity, and dimensionality reduction, which simplifies data while preserving its essential features. This approach is particularly useful in exploratory data analysis, where the goal is to uncover hidden insights without prior knowledge of the outcomes. Applications of unsupervised learning can be found in various fields, including marketing, biology, and finance, helping to reveal trends and relationships in complex datasets.