Unsupervised Learning
Unsupervised learning is a type of machine learning where the algorithm analyzes data without pre-existing labels or categories. Instead of being taught what to look for, it explores the data to identify patterns, group similar items together, and discover hidden structures. For example, it can sort a collection of photos into groups based on shared features or find trends in customer preferences. This approach is valuable for gaining insights from large datasets where the relationships aren’t immediately obvious, enabling further exploration or decision-making.
Additional Insights
-
Unsupervised learning is a type of machine learning where algorithms analyze data without pre-labeled outcomes. Instead of learning from examples that tell them the "right" answer, these algorithms look for patterns, groupings, or structures within the data. For instance, it might cluster customers by purchasing behavior or identify distinct topics in a collection of documents. The goal is to uncover hidden insights and relationships without explicit guidance, allowing for discovery and exploration in complex data sets. This approach is valuable in fields like marketing, biology, and social science, where understanding underlying patterns can inform decisions and strategies.