Artificial Intelligence & Edge Engineering

TinyML on NodeMCU: Training to Deployment (10 Practical Projects)

Train Small AI models using Edge Impulse and deploy them to NodeMCU. Build 10 practical TinyML projects including robotics, voice recognition, and computer vision.

Intermediate 14 Hours English 12 Lessons 0 graduates
Free
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Certificate of Completion Included
What You Will Learn
  • Install and configure the Arduino IDE for NodeMCU deployment across Windows, macOS, and Linux.
  • Train machine learning models using Edge Impulse and export them as Arduino libraries.
  • Deploy 10 practical AI models to hardware, including predictive maintenance, gesture recognition, and autonomous robotics.
  • Write optimized C++ code to interface neural networks with physical hardware sensors and motor drivers.
Requirements
  • A NodeMCU (ESP8266 or ESP32) development board.
  • Basic electronic components (IR sensors, MPU6050 accelerometer, I2S microphone, L298N motor driver, jumper wires).
  • A free account on Edge Impulse for model training.
  • Basic understanding of C++ and Arduino syntax.
Description

The TinyML Edge Deployment Masterclass

This hands-on engineering course teaches you how to train, optimize, and deploy Small AI models (TinyML) directly onto standard, low-cost NodeMCU microcontrollers. You will learn to use Edge Impulse and Google Colab to train neural networks on your PC, compress them into highly efficient C++ headers, and flash them to the edge.

Moving beyond theory, this course features 10 distinct, fully-coded hardware projects. From line-following robots and shape identification to predictive maintenance and voice keyword spotting, you will physically build and code complete AI-driven systems.

Course Curriculum 12 Lessons
Chapter 1: The TinyML Foundation & Environment 2 Lessons

Setting up the local development environment across operating systems and integrating cloud training platforms.

Lesson 1: Arduino IDE Setup (Windows/Linux/Mac)
Lesson 2: The Core Edge Impulse Training Pipeline
Chapter 2: Motion, Kinetics & Autonomous Robotics 2 Lessons

Deploying AI models to control physical movement, from autonomous line following to gesture-based hardware control.

Lesson 3: Use Case 1 - AI-Powered Line Following Robot
Lesson 4: Use Case 2 - Gesture-Controlled Robotics
Chapter 3: Audio & Vibration Processing 2 Lessons

Training the NodeMCU to "listen" and "feel" using microphones and accelerometers for predictive maintenance and voice commands.

Lesson 5: Use Case 3 - Voice Wake-Word Detection
Lesson 6: Use Case 4 - Predictive Maintenance (Vibration)
Chapter 4: Visual & Spatial Intelligence 2 Lessons

Implementing low-resolution computer vision and spatial pattern recognition directly on edge microcontrollers.

Lesson 7: Use Case 5 - Shape Identification (Vision)
Lesson 8: Use Case 6 - Room Occupancy Counting
Chapter 5: Smart Home & Power Grid Analytics 4 Lessons

Deploying anomaly detection and complex pattern recognition models for home automation and power monitoring.

Lesson 9: Use Case 7 - Smart Thermostat (Comfort Pattern)
Lesson 10: Use Case 8 - Fall Detection System
Lesson 11: Use Case 9 - Keyword Spotting for Appliance Control
Lesson 12: Use Case 10 - Anomaly Detection in Power Grids