TinyML and Embedded Machine Learning: From Sensors to Deployment

As devices become smaller and more integrated into our daily lives, the ability to process data locally on microcontrollers is becoming essential. This course introduces you to the intersection of hardware and artificial intelligence, providing a clear path into the growing field of TinyML. You will transition from understanding basic hardware components to deploying functional machine learning models that interpret real-world signals directly on the edge. Through written explanations and structured exercises, you will gain the skills needed to transform raw sensor input into actionable intelligence without relying on cloud connectivity. By the end of this course, you will be able to design and implement efficient models tailored for resource-constrained environments. What you'll learn: - Understand the core principles of embedded systems and low-power hardware architecture - Apply signal processing techniques to interpret raw data from microphones and accelerometers - Build machine learning models optimized for microcontrollers and mobile hardware - Practice model quantization and optimization to reduce memory and storage footprints - Deploy an acoustic event detection system to recognize specific sound patterns - Implement power-efficient inference strategies for sustainable device operation The course begins with essential terminology and hardware basics before moving into data collection, model training, and the specific constraints of edge computing. You will conclude by applying your knowledge to a project focused on classifying real-world acoustic events. This course is designed for beginners interested in the intersection of hardware and AI; no prior experience with embedded systems or machine learning is required. Start building intelligent edge devices today.