Supervised and Unsupervised Learning for Data Science

The textbook "Supervised and Unsupervised Learning for Data Science" offers a comprehensive analysis of the latest learning algorithms, including semi-supervised methods, to present a wide range of clustering and classification solutions for big data applications. It includes case studies and best practices, as well as theoretical models of learning that serve as a comprehensive reference for the field. The eight chapters of the book cover topics such as discretization, feature extraction and selection, classification, clustering, topic modeling, and graph analysis. This work is aimed at practitioners and students, as well as educators who wish to present current approaches to unsupervised and semi-supervised learning in university-level seminars. The content is based on selected, extended contributions from the Fourth International Conference on Soft Computing in Data Science (2018).