Edge AI and TinyML for Microcontrollers

In a world of connected devices, sending all sensor data to the cloud is often slow, costly, and power-intensive. Running machine learning models directly on small hardware—known as Edge AI or TinyML—allows for instant, private, and efficient decision-making right where the data is gathered. This course guides you through the entire lifecycle of embedded machine learning, from understanding hardware constraints to deploying optimized models. You will learn how to adapt standard machine learning workflows for microcontrollers, ensuring your models run efficiently within severe memory and processing limits. What you'll learn: - Understand the core concepts of Edge AI, TinyML, and microcontroller hardware constraints - Process and prepare sensor data specifically for resource-constrained environments - Optimize neural networks using quantization and pruning to minimize memory footprint - Deploy machine learning models to microcontrollers using lightweight C/C++ runtimes - Evaluate model performance, latency, and power consumption on edge hardware Starting with fundamental definitions of embedded systems and machine learning, this text-based course takes you step-by-step through data pipelines, model training concepts, optimization strategies, and real-world deployment scenarios. This course is designed for beginners, software developers, and hardware enthusiasts who want to explore the intersection of AI and embedded systems, requiring no prior experience with machine learning. Start reading today and learn how to build intelligent, low-power embedded applications.