Embedded Computer Vision and TinyML Fundamentals

Computer vision is no longer restricted to powerful cloud servers and high-end computers. With the rise of embedded machine learning, you can now run intelligent image recognition models directly on small, low-power microcontrollers. This course guides you through the process of designing, training, and deploying compact machine learning models to resource-constrained hardware, turning simple cameras into smart sensors. What you'll learn: - Understand the core concepts of digital image processing and how computer vision algorithms interpret visual data. - Explore the fundamentals of embedded machine learning (TinyML) and how neural networks are optimized for microcontrollers. - Prepare and curate custom image datasets, applying modern data augmentation techniques to improve model accuracy. - Train image classification and object detection models using Edge Impulse and optimize them using quantization. - Deploy trained models to embedded platforms like OpenMV to perform real-time visual inference on low-power hardware. - Analyze model performance, latency, and memory usage to ensure efficient on-device execution. You will start by mastering the fundamental theory of digital images and neural networks before moving step-by-step through dataset creation, model training, optimization, and hardware deployment. This course is designed for beginners interested in hardware, IoT, and artificial intelligence, requiring no previous experience with machine learning or embedded systems. Start reading today to bridge the gap between hardware and artificial intelligence.