Building Smart IoT Projects with ESP32 and TinyML
Learn to build intelligent, edge-computing devices from scratch. This project-based course takes a top-down, problem-driven approach to Internet of Things (IoT) and Artificial Intelligence. Instead of getting bogged down in heavy math, you will learn how to collect real-world data, train machine learning models using Edge Impulse, and deploy offline AI directly onto an ESP32 microcontroller. By the end of the course, you will have built a fully functional, smart environmental monitor that processes data locally and communicates efficiently with the cloud.
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About This Course
Learn to build intelligent, edge-computing devices from scratch. This project-based course takes a top-down, problem-driven approach to Internet of Things (IoT) and Artificial Intelligence. Instead of getting bogged down in heavy math, you will learn how to collect real-world data, train machine learning models using Edge Impulse, and deploy offline AI directly onto an ESP32 microcontroller. By the end of the course, you will have built a fully functional, smart environmental monitor that processes data locally and communicates efficiently with the cloud.
A complete hardware kit, including the ESP32 DevKit and all necessary sensors, will be provided to every student for this course.
Silabus Course
01 Foundations of ESP32 and the IoT Ecosystem
Foundations of ESP32 and the IoT Ecosystem
- Introduction to the ESP32 microcontroller architecture and pinouts.
- Setting up the development environment (Arduino IDE / PlatformIO).
- Mini Project: "Hello World" of Hardware – Blinking LEDs and reading basic digital/analog inputs.
02 Sensing the Physical World
Sensing the Physical World
- Interfacing with environmental sensors (Temperature, Humidity, and Accelerometers).
- Understanding data types, sampling rates, and noise filtering.
- Mini Project: Building a basic environmental logger that outputs data to the Serial Monitor.
03 The TinyML Paradigm and Edge Impulse
The TinyML Paradigm and Edge Impulse
- What is Edge AI? Understanding the shift from Cloud ML to Edge ML.
- Introduction to Edge Impulse Studio and the machine learning pipeline.
- Mini Project: Creating an Edge Impulse project and understanding the classification workflow.
04 Data Harvesting and Pre-processing
Data Harvesting and Pre-processing
- The importance of high-quality datasets for TinyML.
- Connecting the ESP32 (or a smartphone) to Edge Impulse to collect raw sensor data.
- Mini Project: Recording and labeling a custom dataset (e.g., motion gestures or audio samples).
05 Training and Validating the AI Model
Training and Validating the AI Model
- Extracting features (e.g., Spectral Analysis for motion or MFCC for audio) without writing complex code.
- Training lightweight Neural Networks designed for microcontrollers.
- Mini Project: Training the classification model in the cloud and testing its accuracy against unseen validation data.
06 Edge Deployment and C++ Integration
Edge Deployment and C++ Integration
- Exporting the trained model as an optimized C++ library.
- Integrating the AI library into the ESP32 codebase.
- Mini Project: "Offline Intelligence" – Running real-time inference on the ESP32 to detect patterns without an internet connection.
07 IoT Telemetry and Cloud Dashboards
IoT Telemetry and Cloud Dashboards
- Connecting the smart device to the internet (WiFi or Cellular).
- Sending inference results (insights), rather than raw data, to a backend system to save bandwidth.
- Mini Project: Pushing AI-triggered alerts to a cloud database or IoT dashboard.
08 Power Management and Real-World Assembly
Power Management and Real-World Assembly
- Transitioning from breadboards to standalone devices.
- Introduction to ESP32 Deep Sleep modes to conserve battery life.
- Course Wrap-up: Final review of the architecture and preparation for the Capstone.
Capstone Project
EcoSentinel: Smart Acoustic & Environmental Monitor
The Capstone Project challenges students to build an end-to-end, intelligent monitoring system designed for real-world application, such as agricultural monitoring, predictive maintenance, or home security. Students will interface an I2S microphone with the ESP32, collect specific audio samples (e.g., machine anomalies, glass breaking, or pest noises), and train a custom TinyML model. The final device will actively listen to its environment offline. When an anomaly is detected with high confidence, the ESP32 will wake up its network module and transmit a lightweight alert payload to a cloud dashboard.
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Team Discounts
Get special pricing for group registrations. The more participants, the bigger the discount.
Custom Curriculum
Training materials can be tailored to your team's specific needs and company projects.
Flexible Schedule
Choose training times that suit your team: weekday, weekend, or special sessions at your office.
Official Certificate
All participants receive a professional certificate upon completion.
Post-Training Support
Get free consultation access for 30 days after training to ensure successful implementation.
Real Projects
Participants will work on real-world projects that can be immediately applied in their work environment.
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Frequently Asked Questions
Find answers to common questions about our training programs
Yes, we provide online (remote), offline (at your office, for corporate only), or hybrid training options based on your team's needs. All formats receive the same materials and certificates.
For corporate training, the minimum is 3 participants. However, we also accept individual registrations.
Absolutely. We offer custom curriculum services where materials can be tailored to your technology stack, active projects, and your team's specific needs.
Yes, we offer special group discounts: 10% for 5-9 participants, 15% for 10-14 participants, and 20-30% for 15+ participants from the same company.
Training duration varies depending on the material. For corporate training, schedules can be customized to your team's needs - weekday, weekend, or custom schedules.
Yes, all participants who complete the training will receive an official certificate from Rumah Coding. Digital certificates can be verified online.
Of course. We provide free consultation support for 30 days after training to help with implementation. Participants also get access to our exclusive community and training recording materials.
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