ESP32 Machine Learning Projects for Edge Intelligence : Designing Intelligent IoT Systems with Embedded Machine Learning, Real-Time Data Processing, and Smart Device Automation

Brenda K. Gaskin

ISBN 13: 9798184315249

Edité par Independently Published Jun 2026, 2026

A propos de cet article

Neuware - Have you ever wondered how a tiny, low-cost microcontroller can recognize spoken commands, detect defects on a production line, or predict equipment failures before they happen What if you could build intelligent systems that perform these tasks without relying on expensive cloud infrastructure, recurring subscription fees, or a constant internet connection Embedded AI and TinyML is a practical, hands-on guide to designing, training, and deploying machine learning models directly on embedded devices. Whether you are an engineer, student, maker, or technology professional, this book provides the knowledge and tools needed to build real-world intelligent systems that operate efficiently at the edge.Inside this book, you will learn how to: Build reliable sensor data pipelines for vibration, audio, temperature, motion, and environmental monitoring applications, using proven filtering, calibration, and preprocessing techniques.Design and train compact machine learning models optimized for resource-constrained hardware through quantization, pruning, compression, and other model optimization strategies.Deploy practical AI applications including image classification, keyword spotting, anomaly detection, predictive maintenance, and intelligent monitoring systems.Combine multiple sensors into context-aware solutions that integrate data from different sources to improve accuracy and decision-making.Integrate edge intelligence with cloud services for remote monitoring, analytics, over-the-air updates, and large-scale device management.Implement security and power optimization techniques including encryption, secure boot, privacy-aware design, and energy-efficient operation for battery-powered systems.Rather than focusing on theory alone, this book emphasizes practical implementation through real-world examples, step-by-step projects, and deployment strategies that mirror professional engineering workflows.Whether you are exploring embedded AI for the first time, advancing beyond basic tutorials, or developing products intended for real-world deployment, this guide will help you move from simple sensor experiments to intelligent, dependable systems that deliver value where it matters mostdirectly on the device.Start building the next generation of smart, connected, and autonomous systems today.

N° de réf. du vendeur 9798184315249

Détails bibliographiques

• Titre: ESP32 Machine Learning Projects for Edge Intelligence : Designing Intelligent IoT Systems with Embedded Machine Learning, Real-Time Data Processing, and Smart Device Automation
• Éditeur: Independently Published Jun 2026
• Date de parution: 2026
• Langue: anglais
• ISBN à 13 chiffres: 9798184315249
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 279 L x 216 H x 9 P (en millimètres) 279 millimètres de largeur par 216 millimètres de hauteur par 9 millimètres de profondeur
• Poids: 420 grammes

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Synopsis

Have you ever wondered how a tiny, low-cost microcontroller can recognize spoken commands, detect defects on a production line, or predict equipment failures before they happen? What if you could build intelligent systems that perform these tasks without relying on expensive cloud infrastructure, recurring subscription fees, or a constant internet connection?

Embedded AI and TinyML is a practical, hands-on guide to designing, training, and deploying machine learning models directly on embedded devices. Whether you are an engineer, student, maker, or technology professional, this book provides the knowledge and tools needed to build real-world intelligent systems that operate efficiently at the edge.

Inside this book, you will learn how to:

Build reliable sensor data pipelines for vibration, audio, temperature, motion, and environmental monitoring applications, using proven filtering, calibration, and preprocessing techniques.

Design and train compact machine learning models optimized for resource-constrained hardware through quantization, pruning, compression, and other model optimization strategies.

Deploy practical AI applications including image classification, keyword spotting, anomaly detection, predictive maintenance, and intelligent monitoring systems.

Combine multiple sensors into context-aware solutions that integrate data from different sources to improve accuracy and decision-making.

Integrate edge intelligence with cloud services for remote monitoring, analytics, over-the-air updates, and large-scale device management.

Implement security and power optimization techniques including encryption, secure boot, privacy-aware design, and energy-efficient operation for battery-powered systems.

Rather than focusing on theory alone, this book emphasizes practical implementation through real-world examples, step-by-step projects, and deployment strategies that mirror professional engineering workflows.

Whether you are exploring embedded AI for the first time, advancing beyond basic tutorials, or developing products intended for real-world deployment, this guide will help you move from simple sensor experiments to intelligent, dependable systems that deliver value where it matters mostdirectly on the device.

Start building the next generation of smart, connected, and autonomous systems today.