Foundations of Embedded Machine Learning and TinyML

Machine learning is no longer confined to powerful servers; it is moving to the very edge of our physical world through low-power devices. This course provides a comprehensive introduction to the field of TinyML, enabling you to bring intelligence to hardware that operates on a battery for months or even years. You will learn how to bridge the gap between data science and embedded systems to create responsive, privacy-focused, and efficient applications. By the end of this course, you will understand the full lifecycle of an embedded machine learning project, from data collection to on-device inference. What you'll learn: - Understand the fundamental principles of TinyML and edge computing architectures. - Learn how to collect and preprocess sensor data specifically for embedded environments. - Practice training neural networks optimized for resource-constrained microcontrollers. - Apply model optimization techniques like quantization and pruning to minimize memory footprints. - Explore modern frameworks such as TensorFlow Lite for Microcontrollers for edge deployment. - Understand how to implement real-world use cases like keyword spotting and gesture recognition. The course begins with essential terminology and an overview of embedded hardware before guiding you through data pipelines, model optimization, and deployment strategies. This program is designed for beginners interested in electronics or software who want to explore the intersection of AI and hardware, with no prior machine learning experience required. Start your journey into the world of intelligent embedded systems today.