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 relies on big data to discover 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 are developed throughout this book from a methodological and practical perspective with applications through the Python software. The following techniques are covered: dimension reduction, principal components analysis, factor analysis, simple correspondence analysis, multiple correspondence analysis, multidimensional scaling, neural networks (SOM Kohonen, etc.), pattern recognition, anomaly detection, autoencoders, image processing, and convolutional neural networks (CNNs).