This package introduces participants to the use of machine learning crack detection techniques for predicting metal fatigue crack growth in aluminum lap joints using Lamb wave signal data. Participants will work with the PHM 2019 Aluminum Lap Joint Fatigue Dataset, perform signal-based feature extraction, and build ensemble learning models to estimate crack progression. Through theoretical explanation and hands-on Python implementation, learners will gain practical experience in applying crack detection using machine learning and other data-driven methods for structural health monitoring (SHM) and predictive maintenance of metallic structures.