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hardware Advanced 21 lessons

Edge AI (TinyML)

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.

✅ 100% Free & Lifetime Access
⏱️ 5-Minute Lessons (Bite-sized learning)
🚀 21-Lesson Path (Independent modules)
📱 Mobile Friendly (Learn anywhere)
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Complete Course Syllabus

  • 1
    TinyML Concepts
    Constraints of running AI on <256KB RAM.
  • 2
    Data Collection
    Gathering sensor data for training sets.
  • 3
    Training & Optimization
    Quantizing models from Float32 to Int8.
  • 4
    Deployment
    Running inference loops on Arduino C++.
  • 5
    Projects
    Building a voice-controlled switch and magic wand.

Estimated completion time: 21 lessons • Self-paced learning • Lifetime access

Career Outlook

Estimated Salary
$130k - $180k

Career Paths

Edge AI Engineer $135k-$185k
Embedded ML Eng $130k-$180k
IoT Architect $140k-$190k

What You Will Learn

Train ML models specifically for microcontroller constraints
Deploy TensorFlow Lite for Microcontrollers models
Implement Keyword Spotting (Voice) on device
Recognize gestures using accelerometer data
Optimize models using Quantization and Pruning

Skills You Will Gain

TinyML TensorFlow Lite Embedded C++ Signal Processing Model Optimization

Who Is This For

Embedded Engs
ML Researchers
IoT Architects

Prerequisites

Python ML Basics
Arduino

Edge AI (TinyML) FAQs

Hardware?

Arduino Nano 33 BLE Sense is recommended.

Math?

Understanding neural nets helps, tools simplify it.

Is it slow?

Surprisingly fast for specific tasks (DSP).

Cloud needed?

No, inference happens 100% offline on chip.

Need more info?
All Reork courses are 100% free, self-paced, and fully accessible on mobile devices. No prior experience is required. Read our full Platform FAQ.

Tech Stack & Tools

Edge Impulse
TensorFlow Lite
Arduino Nano BLE
Colab

Recommended Next Steps

FPGA Programming (Verilog)

hardware Advanced

Design digital hardware. Master Verilog HDL to program FPGAs, simulate logic gates, and implement custom processors.

ESP32 WiFi/Bluetooth

hardware Intermediate

Build connected devices. Master the ESP32 microcontroller to create web servers, use Bluetooth (BLE), and connect to the cloud.

3D Printing & CAD

hardware Beginner

Turn ideas into physical objects. Master Fusion 360 for modeling and Slicer settings for high-quality FDM and Resin 3D printing.

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