Machine Learning with Arduino Course
Master TinyML on Arduino to transform raw sensor data into smart, real-time decisions. This course covers signal conditioning, feature extraction, lightweight neural networks, model compression, deployment to Arduino sketches, and on-device testing, enabling you to build efficient, production-ready embedded intelligence for electronics projects with confidence and speed.
4 to 360h flexible workload
certificate valid in your country
What will I learn?
This course teaches how to capture and condition sensor data, engineer efficient features, and develop compact TinyML models for reliable operation on microcontrollers like Arduino. You will master FFT and time-domain techniques, lightweight neural networks, model compression, deployment to Arduino sketches, and on-device testing to create accurate, low-power smart systems quickly.
Elevify advantages
Develop skills
- TinyML model design: build compact classifiers tailored to Arduino limits.
- Sensor ML pipeline: acquire, filter, and window raw signals for learning.
- Edge deployment: export, quantize, and embed ML models into Arduino sketches.
- Real-time inference: code fast loops, interrupts, and low-latency ML decisions.
- On-device evaluation: profile timing, memory, accuracy, and failure modes.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students are saying
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