Machine Learning (“ML”) techniques, a sub-field of Artificial Intelligence, become increasingly popular within the financial industry to tackle issues involving large amounts of data. In this paper we aim at providing the reader with a basic introduction to key ML concepts and techniques, explain how such approaches differ from the more traditional statistical analysis approach and illustrate this theoretical presentation with some simple yet very practical application within the financial industry. We also illustrate our point with a case-study: a practical application of decision trees to build predictive LGD models in loan books management. We wrap-up this paper with a high-level comparison between traditional statistical inference methods and ML techniques.