Embedded AI & AIoT: Building Intelligent Edge Systems

<div>This course contains the use of artificial intelligence.</div><div><br></div><div>This course is designed to give you a complete, hands-on understanding of Embedded AI and AIoT systems, covering the full journey from raw sensor data to deployed intelligence on microcontrollers.</div><div><br></div><div>Unlike courses that focus only on machine learning theory or only on embedded programming, this course takes a system-level approach. You will learn how sensing, data acquisition, machine learning, embedded deployment, wireless communication, and power optimization come together in real-world AIoT products.</div><div><br></div><div>You will start with strong foundations in IoT, Edge AI, and Embedded Machine Learning, followed by practical Python-based workflows for data analysis and ML. Step by step, you will explore sensor data processing, feature engineering, ML model development, and optimization for embedded targets. You will then move into deploying AI models on microcontrollers, running on-device inference, and validating system performance.</div><div><br></div><div>The course also covers sensor integration (UART, I2C, SPI), wireless communication strategies, and low-power optimization, which are critical for real-world embedded AI systems. Throughout the course, concepts are reinforced using hands-on labs, guided solutions, structured learning modules, and practical demonstrations.</div><div><br></div><div>This course is ideal for students, engineers, and professionals who want to move beyond theory and build real Embedded AI and AIoT systems with confidence.</div><div><br></div><div>By the end of this course, you will not just understand Embedded AI — you will know how to design, deploy, test, and optimize intelligent edge systems used in industry today.</div>

What you'll learn:

• Design and build complete Embedded AI & AIoT systems from sensor data acquisition to on-device intelligence.
• Develop and optimize machine learning models for sensor data suitable for deployment on microcontrollers.
• Deploy and run AI models on embedded hardware, enabling real-time, on-device inference.
• Test, validate, and optimize embedded AI systems for performance, reliability, and low-power operation