AI projects smart home devices machine learning hardware — Build connected devices with on-board intelligence, from smart-home sensors to predictive monitoring, combining machine learning with real IoT hardware. This hands-on guide takes you from beginner to pro, crafting 12 practical projects that integrate AI directly into smart devices. You'll learn to deploy lightweight ML models on microcontrollers, create sensor arrays that predict maintenance needs, and design voice-controlled assistants that run offline. Each project includes circuit diagrams, code listings, and troubleshooting tips. Topics cover edge computing, real-time data processing, and low-power optimization. By the end, you'll have a portfolio of working prototypes and the skills to innovate further. Unlike [placeholder] and [placeholder], this book emphasizes on-board AI without cloud dependency, ensuring privacy and low latency. What You'll Build: Smart thermostat with occupancy learning Predictive maintenance monitor for appliances Gesture-controlled lighting system Voice-activated security camera with local processing Soil moisture sensor with irrigation forecasting Air quality analyzer with anomaly detection Wearable health tracker with fall detection Smart lock with facial recognition Energy meter with consumption prediction Pet feeder with behavioral analysis Plant health monitor with disease prediction Multi-sensor gateway with AI fusion Each project uses affordable components like Raspberry Pi Pico, ESP32, or Arduino Nano. You'll implement decision trees, k-nearest neighbors, and lightweight neural networks using TensorFlow Lite and Edge Impulse. The book covers sensor calibration, wireless communication (Wi-Fi, BLE, LoRa), and power management. Debugging techniques and performance benchmarks help you refine designs. Perfect for hobbyists, students, and engineers entering the IoT AI space.
