 This paper provides a comprehensive review of existing machine learning interpretability methods, including their underlying principles, applications, and implementation details. The authors also provide a taxonomy of these methods, which can serve as a reference point for both researchers and practitioners. This article was authored by Pentelus Liner-Dottos, the Silas Papastophanopoulos, and Sotiris Cotsiantis.