This course offers an introduction to the fundamental concepts of machine learning, together with an analysis of the main techniques and algorithms used in this discipline. The analysis of theoretical frameworks will be complemented by the development of concrete examples in Python and related machine learning libraries.

Topics covered include the theoretical foundations of data learning, the design of machine learning projects, the procedure for training, evaluating and validating machine learning models, as well as classical learning paradigms and related algorithms.