TinyML Applications for Embedded Devices

Machine learning is no longer confined to massive data centers; it is now powering the smallest devices in our daily lives. This course provides a practical foundation in TinyML, enabling you to build intelligent features for hardware with limited memory and power. You will learn how to bridge the gap between complex algorithms and constrained embedded systems. Through written explanations and code-based examples, you will explore how to process sensor data to make real-time decisions on the edge. What you'll learn: - Understand the core principles and constraints of edge computing and TinyML terminology - Implement keyword spotting systems for voice-activated device commands - Apply visual wake word techniques to identify specific objects or people using low-power sensors - Develop gesture recognition models using motion data from accelerometers and gyroscopes - Optimize models using quantization and pruning to fit within strict hardware limits - Explore modern MLOps workflows for deploying and monitoring models on remote edge devices The course begins with foundational concepts of embedded AI before diving into specific applications for audio, vision, and motion data. You will follow a structured path from understanding raw sensor input to deploying an optimized model on a microcontroller. This course is designed for beginners interested in AI and hardware, requiring no prior experience with machine learning deployment. Start your journey into the world of intelligent edge computing today.