Tiny Machine Learning Quickstart: Machine Learning for Arduino Microcontrollers

“Tiny machine learning quickstart by Salvatore Salerno is a practical and approachable introduction to deploying machine learning (ML) models on resource-constrained embedded devices. … Tiny machine learning quickstart is a valuable guide for anyone interested in running ML models directly on microcontrollers. Its clear explanations, Arduino-centered projects, and thoughtful treatment of embedded constraints make it a timely contribution to the growing body of TinyML resources.” (Wael Badawy, Computing Reviews, March 16, 2026)

Be a part of the Tiny Machine Learning (TinyML) revolution in the ever-growing world of IoT. This book examines the concepts, workflows, and tools needed to make your projects smarter, all within the Arduino platform.

You’ll start by exploring Machine learning in the context of embedded, resource-constrained devices as opposed to your powerful, gigabyte-RAM computer. You’ll review the unique challenges it poses, but also the limitless possibilities it opens. Next, you’ll work through nine projects that encompass different data types (tabular, time series, audio and images) and tasks (classification and regression). Each project comes with tips and tricks to collect, load, plot and analyse each type of data.

Throughout the book, you’ll apply three different approaches to TinyML: traditional algorithms (Decision Tree, Logistic Regression, SVM), Edge Impulse (a no-code online tools), and TensorFlow for Microcontrollers. Each has its strengths and weaknesses, and you will learn how to choose the most appropriate for your use case. TinyML Quickstart will provide a solid reference for all your future projects with minimal cost and effort.

You will:

• Navigate embedded ML challenges
• Integrate Python with Arduino for seamless data processing
• Implement ML algorithms
• Harness the power of Tensorflow for artificial neural networks
• Leverage no-code tools like Edge Impulse
• Execute real-world projects