TinyML Fundamentals: Machine Learning on Microcontrollers

Unlock the power of artificial intelligence on tiny, battery-powered devices. This course demystifies the world of Tiny Machine Learning (TinyML), where ML models run locally on small, energy-efficient microcontrollers. By the end of this course, you will have a solid foundation in the principles and practices of TinyML. You'll understand how to create intelligent devices that can sense, process, and act on data from the physical world without relying on a constant internet connection. What you'll learn: - Understand the core concepts of TinyML and its applications in everyday objects. - Learn the end-to-end workflow using TensorFlow Lite for Microcontrollers. - Practice training simple neural networks suitable for resource-constrained hardware. - Apply model optimization and quantization techniques to shrink models for deployment. - Explore the basics of working with microcontrollers and sensors for data collection. - Build a complete TinyML application, from data gathering to on-device inference. The course begins with key terminology and foundational concepts of both machine learning and embedded systems. From there, you'll progress through the practical steps of building and deploying your first intelligent device. This course is designed for complete beginners. No prior experience in machine learning or embedded electronics is required to get started. Begin your journey into the exciting field of on-device AI today.