Machine learning is an interdisciplinary field that uses methods, algorithms, processes, and systems to extract knowledge and conclusions from structured and unstructured data. It combines elements of statistics, computer science, mathematics, and analytical techniques to solve problems, make predictions, and generate value from data. It leverages big data to uncover patterns, trends, and relationships that can be used for decision-making in various industries. It is an important support for Artificial Intelligence. Machine learning uses two types of techniques: supervised learning, which trains a model with known input and output data to predict future outcomes, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data. Most of these unsupervised learning techniques for classification are developed throughout this book from a methodological point of view and from a practical point of view with applications through the R software. The following techniques are covered: simple correspondence analysis, multiple correspondence analysis, cluster analysis, multidimensional scaling, neural networks (SOM Kohonen, etc.), pattern recognition, anomaly detection, autoencoders, image processing and convolutional neural networks (CNN networks).