Machine Learning with Arduino Course
Master TinyML on Arduino to transform raw sensor data into smart, real-time decisions. Gain skills in signal conditioning, feature extraction, lightweight neural networks, model compression, and seamless deployment to Arduino for efficient, production-ready embedded systems in electronics projects.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
This course teaches capturing and conditioning sensor data, engineering efficient features, and building compact TinyML models for reliable microcontroller operation. Explore FFT and time-domain methods, lightweight neural networks, model compression, Arduino sketch deployment, and on-device testing to develop accurate, low-power smart systems with confidence.
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 say
As suas aulas são perfeitas. Adquiri o pacote de um ano e, finalmente, tenho a oportunidade de acompanhar diversos temas do meu interesse sem precisar mudar de plataforma... agradeço por tudo o que fazem, já recomendei vocês a outras pessoas...

Giulio CarloAluno de Marketing Digital
Eu gosto de como as lições são direto ao ponto e como eu consigo alterar capítulos e pular conteúdos que não preciso.

Mariana FerresAluna de Fotografia
Gosto do conteúdo e do modo de apresentação e transcrição de vídeos, o que acelera o processo!

Luciana AlvarengaAluna de Design de Unhas
A plataforma é rápida, simples de usar. A diversidade de conteúdo e os vídeos complementares ajudam muito no aprendizado.

Giulio CarloAluno de Engenharia de Prompt
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