Master TinyML, Sensor AI, Audio
Wake-Word ML
& Embedded Intelligence
Course Overview
This 4-week immersive training program blends 1.5 hours of theory
with 4 hours of hands-on lab sessions daily. Participants gain practical
skills in Edge AI, TinyML, Sensor ML, Audio Wake-Word Detection, and
deployment of AI models on embedded platforms like Arduino Nano
33 BLE Sense and ESP32.
The course concludes with a full end-to-end industry project showcasing
real-time embedded inference.
Learning Outcomes
- Collect & preprocess IMU and audio data
- Perform time-series analysis and feature extraction
- Use MFCC for audio processing
- Train ML models using TensorFlow/Keras
- Convert & optimize models using TensorFlow Lite
- Deploy models on microcontrollers for real-time inference
- Build a complete Edge AI solution from data acquisition to deployment
Audio Wake-Word Module
- Audio recording & preprocessing
- MFCC and spectrogram extraction
- Training small-footprint wake-word models
- Deploying audio models using TFLite Micro
- Real-time keyword detection on embedded devices
Career Outcomes – Job Roles
- Embedded AI Engineer
- Edge AI / TinyML Developer
- IoT ML Engineer
- Audio ML Engineer (Wake-word / Voice ML)
- Sensor Data & Signal Processing Engineer
- Firmware Engineer – AI Enabled Devices
- R&D Engineer – Smart & Intelligent Systems
- Robotics / Automation AI Developer
Career Outcomes – Domains
- Wearable Technology
- Smart Devices & Consumer IoT
- Industrial IoT & Smart Manufacturing
- Automotive Embedded Systems
- Healthcare / Wellness Devices
- Robotics & Automation
- AIoT and Edge Computing Startups
Why Now? (Industry Demand & Future
Growth)
- Edge AI market projected to exceed $90 billion by 2030
- TinyML adoption rising rapidly due to low-power, on-device intelligence
- Transition from cloud-based AI → real-time Edge AI
- High demand for low-latency, privacy-preserving AI
- Growing use of voice interfaces, smart sensors, and intelligent wearables
- Shortage of engineers skilled in the AI + Embedded Systems combination
Tools & Technologies
- Python: NumPy, Pandas, Matplotlib
- TensorFlow, Keras, TensorFlow Lite
- MFCC & audio processing libraries
- Arduino Nano 33 BLE Sense, ESP32
- Embedded C/C++ for deployment
PREREQUISITE
| Category |
Prerequisite |
Level |
| Programming |
Python & c/c++ fundamentals |
Basic |
| Math & Data |
Algebra, statistics, vectors |
Basic |
| Embedded Systems |
Arduino or MUC Programming |
Beginner-Intermediate |
| Tools |
Python IDEs, Aeduino IDE |
Beginner |
