Why Read This Book

You will learn how to build practical TinyML systems on Arduino microcontrollers without getting lost in theory, with MATLAB used to bridge model development, simulation, and deployment. The book is especially useful if you want to move from machine learning concepts to real embedded implementations, where memory, latency, power, and sensor data quality actually matter.

Who Will Benefit

Embedded developers, IoT engineers, and students with basic programming and electronics knowledge who want to deploy lightweight ML models on Arduino-based hardware using MATLAB.

Level: Intermediate — Prerequisites: Basic programming knowledge, familiarity with Arduino or microcontroller concepts, and a working understanding of sensors, data collection, and simple linear algebra or statistics.

Key Takeaways

• Design TinyML workflows for constrained Arduino-class devices
• Collect, prepare, and label sensor data for embedded ML
• Train and evaluate lightweight models suitable for microcontrollers
• Use MATLAB to prototype, analyze, and validate TinyML pipelines
• Deploy inference logic onto Arduino hardware with attention to memory and compute limits
• Debug and iterate on embedded ML applications using realistic sensor-driven examples

Topics Covered

1. Introduction to TinyML and embedded machine learning
2. Arduino microcontroller fundamentals for TinyML
3. MATLAB workflow for data acquisition and model development
4. Sensor data collection, preprocessing, and feature engineering
5. Building and evaluating lightweight ML models
6. Model optimization for memory, speed, and power constraints
7. Deploying models to Arduino microcontrollers
8. Real-time inference and embedded application integration
9. Case studies and end-to-end TinyML projects
10. Testing, debugging, and performance tuning
11. Extending TinyML systems with IoT and edge workflows
12. Best practices, limitations, and future directions

Languages, Platforms & Tools

How It Compares

Covers similar practical deployment territory to books like "TinyML" by Warden and Situnayake, but leans more toward MATLAB-centered workflows and Arduino implementation details.