At MLAIA Data Science, we have spent years in both industry and academia, developing a keen understanding of how classical digital signal processing techniques can significantly boost the performance of machine learning (ML) and AI models. Despite the influx of new algorithms and tools, we strongly advocate for the indispensable power of foundational techniques such as Fourier transforms, filters, and the Nyquist-Shannon Sampling Theorem. Using classical signal processing can be the Make-or-Break Factor in Machine Learning Success. Let’s delve deeper into how we leverage these principles to optimise our models and outcomes.