Unsupervised Learning
Unsupervised learning is a type of ML where the algorithm is trained on unlabeled data, meaning that there are no predefined outputs. Instead of learning from explicit feedback, the model tries to identify patterns, relationships, or structures within the data on its own. This method is particularly useful for exploratory data analysis and pattern discovery.
Clustering and dimensionality reduction are two primary types of unsupervised learning. Clustering techniques, such as k-means and hierarchical clustering, group similar data points together based on their features. These techniques are commonly used in market segmentation, customer profiling, and anomaly detection.
Dimensionality reduction methods, like Principal Component Analysis (PCA) and t-SNE, help simplify complex datasets by reducing the number of input variables while retaining essential information. This is crucial for improving computational efficiency and visualization in high-dimensional data.
Unsupervised learning plays a significant role in recommendation systems, genetic data analysis, network security, and document classification, where hidden patterns need to be uncovered without explicit human supervision.