Unsupervised learning algorithms are used to find patterns and relationships in data without any prior knowledge or labeled data. These algorithms are particularly useful for data exploration and clustering tasks.
K-Means Clustering: A popular algorithm for grouping data points into clusters based on their similarities.
Principal Component Analysis (PCA): A dimensionality reduction technique that identifies the most important features in a dataset and reduces its complexity.
Support Vector Machines (SVM): A powerful algorithm for classification tasks that finds the best hyperplane to separate data points into different classes.
Deep Learning: A subset of machine learning that uses artificial neural networks to learn from data and make predictions.
