This comprehensive course introduces participants to advanced techniques in automotive signal diagnostics, focusing on the integration of wavelet transforms and machine learning. Starting with foundational concepts in signal processing, the course explores wavelet-based denoising, feature extraction, and data-driven diagnostic methods. Participants will gain hands-on knowledge in applying machine learning algorithms for fault detection and system health monitoring. Through real-world case studies and practical applications, learners will develop the skills needed to analyze complex automotive signals and enhance diagnostic accuracy.