Master New Skills with 5-Minute Bite-Sized Learning Modules
Start your journey for free.
Master New Skills with 5-Minute Bite-Sized Learning Modules
Start your journey for free.
Run AI on microcontrollers. Train and deploy TensorFlow Lite models to Arduino and ESP32 for voice, gesture, and vision recognition.
AI is moving to the edge. TinyML allows machine learning models to run on battery-powered microcontrollers with kilobytes of RAM. This course teaches you to train models using Edge Impulse or TensorFlow, optimize them (quantization) for small devices, and deploy them to Arduino/ESP32. You will build projects like keyword spotting (voice control), gesture recognition (magic wand), and anomaly detection for predictive maintenance.
Estimated completion time: 21 lessons • Self-paced learning • Lifetime access
Arduino Nano 33 BLE Sense is recommended.
Understanding neural nets helps, tools simplify it.
Surprisingly fast for specific tasks (DSP).
No, inference happens 100% offline on chip.
Go from your first step to master level. Three simple steps, all about Edge AI (TinyML).
Build a strong base in Edge AI (TinyML). No experience needed — just start.
This is where Edge AI (TinyML) gets exciting — deeper skills, real results.
Top learners use Replit Agent to build working apps from a description. Reach master level in less time.