Unsupervised Learning

Unsupervised learning is a type of machine learning where the input data has no labels or target output, and the goal is to learn the underlying structure and patterns in the data. Two common types of unsupervised learning algorithms are clustering and dimensionality reduction.

Clustering is a process of grouping similar data points together based on some similarity measure. The main goal of clustering is to find groups (clusters) of data points that are similar to each other, while being dissimilar to data points in other clusters. Clustering algorithms can be used for various tasks such as data exploration, customer segmentation, image segmentation, and anomaly detection.

The most common clustering algorithms are K-means clustering and hierarchical clustering. K-means clustering algorithm involves randomly selecting K centroids and assigning each data point to the nearest centroid. The centroids are then updated by taking the mean of all data points assigned to that centroid, and the process is repeated until the centroids no longer move. Hierarchical clustering algorithms involve recursively combining data points or clusters based on some similarity measure until a single root cluster is formed.

Dimensionality reduction is a process of reducing the number of input features while preserving the most important information in the data. This is particularly useful for high-dimensional datasets where it is difficult to visualize the data or perform further analysis. The main goal of dimensionality reduction is to reduce the complexity of the data and remove any redundant or irrelevant features.

The most common dimensionality reduction algorithms are Principal Component Analysis (PCA) and t-SNE. PCA finds the orthogonal directions that explain the most variance in the data, and projects the data onto a lower-dimensional space. t-SNE is a non-linear dimensionality reduction algorithm that preserves the local structure of the data, and is particularly useful for visualizing high-dimensional data in 2D or 3D space.

Unsupervised learning algorithms can be used for various tasks such as data exploration, anomaly detection, and feature engineering. In data exploration, clustering algorithms can be used to discover patterns and relationships in the data that were not previously known. In anomaly detection, clustering algorithms can be used to identify data points that are significantly different from other data points in the dataset. In feature engineering, dimensionality reduction algorithms can be used to remove redundant or irrelevant features from the data, and to transform the data into a more suitable representation for downstream tasks.