TinyML Fundamentals: Machine Learning on Embedded Devices

Smart devices are everywhere, but running complex AI models on tiny hardware requires a specialized set of skills. This text-based course bridges the gap between machine learning and embedded systems, showing you how to bring intelligence to everyday objects. You will transition from understanding standard machine learning to deploying highly optimized models on low-power hardware, gaining a practical understanding of how to shrink models without sacrificing critical performance. What you'll learn: - Understand the core terminology of machine learning and embedded hardware constraints. - Apply quantization and pruning techniques to reduce model size for microcontrollers. - Configure TensorFlow Lite for Microcontrollers to run inference on tiny devices. - Analyze power consumption and memory limitations of edge hardware. - Practice writing clean, efficient code for embedded AI applications. The course begins with foundational concepts of embedded systems and machine learning, guiding you step-by-step through model optimization, compilation, and final deployment on hardware. You will learn through clear written explanations, practical code walkthroughs, and conceptual exercises. Designed for beginners in embedded systems or machine learning who want to explore the intersection of both fields, no advanced hardware or prior ML experience is required to get started. Start reading today to unlock the potential of intelligent, low-power devices.