Introduction to TinyML: Machine Learning on Microcontrollers

Smart devices are everywhere, but running complex machine learning models on tiny, low-power hardware requires a unique set of skills. TinyML bridges the gap between embedded systems and artificial intelligence, enabling intelligence directly on the edge. This text-based course guides you from the absolute basics of hardware and machine learning to deploying optimized models on microcontrollers. You will understand how to shrink models without losing accuracy and run them efficiently in real-world scenarios. What you'll learn: Understand the core principles of TinyML, embedded systems, and resource-constrained computing; Explore model optimization techniques including quantization, pruning, and clustering; Prepare and preprocess sensor data for edge-based machine learning applications; Build and train compact neural networks using modern framework conventions; Deploy optimized models onto popular microcontroller architectures and edge hardware; Troubleshoot, test, and evaluate model performance and power consumption on-device. You will start with foundational definitions and key terminology of embedded systems and machine learning. From there, you will progress through data collection, model training, optimization strategies, and step-by-step deployment workflows on microcontrollers. This course is designed for beginners in machine learning, software developers, and electronics enthusiasts who want to explore edge AI. No prior experience with hardware or advanced mathematics is required. Start your journey into the world of edge intelligence and learn to build smart, low-power applications today.