 This study presents a literature review and taxonomy of machine learning interpretability methods, with links to their programming implementations, to serve as a reference point for both theorists and practitioners in the field of explainable artificial intelligence, XAI, which aims to develop new methods that explain and interpret machine learning models. This article was authored by Pantelus Leonardo's, Vasilis Papastophanopoulos, and Sotiris Cociandis.