Data science is an interdisciplinary field that uses methods, algorithms, processes, and systems to extract knowledge and conclusions from all types of data. Through machine learning, it combines elements of statistics, computer science, mathematics, and analysis techniques to solve problems, make predictions, and generate value from data. It relies on large volumes of data (big data) to discover patterns, trends, and relationships that can be used for decision-making. Machine learning uses two types of techniques: supervised learning, which trains a model with known input and output data so that it can predict future results, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data. Supervised learning uses classification and regression techniques based on predictive models, depending on the nature of the dependent variable. If it is categorical, we are dealing with predictive classification techniques, and if it is quantitative, we are dealing with predictive regression techniques. This book develops supervised learning techniques, delving into classifiers such as the KNN (Nearest Neighbor) algorithm, the SVM (Support Vector Machine) algorithm, and the Naive Bayes algorithm. Next, model assembly techniques such as Boosting, Bagging, Stacking, Voting, and Blending are addressed. Finally, advanced topics such as neural network models for classification are developed. Architectures such as the Multilayer Perceptron, the Radial Basis Network, Adaline Networks, Hopfield Networks, and neural networks for time series prediction (LSTM networks, RNN recurrent networks, GRU networks, and NARX networks) are taken into account. For all topics, methodological concepts are presented and illustrated with practical examples and exercises fully solved in Python code.