Course Description

Course Description (Single Paragraph) This hands-on course provides a comprehensive foundation in the Internet of Things (IoT), connectivity, and applied artificial intelligence, enabling learners to design intelligent, real-time systems. It begins with IoT hardware fundamentals using ESP32, including sensor interfacing and actuator control, followed by device connectivity through Wi-Fi and cloud platforms for data transmission, visualization, and remote operation. The course then focuses on practical machine learning for IoT, emphasizing real sensor data handling, feature engineering, and the development of lightweight models such as Decision Trees, KNN, and Logistic Regression using Python. Finally, learners integrate AI with IoT systems to enable real-time predictions, anomaly detection, and automated decision-making. Through mini projects and a capstone project, students gain the ability to build smart applications such as smart irrigation, energy monitoring, and environmental intelligence systems. Course Learning Outcomes (CLOs) By the end of this course, learners will be able to: Understand IoT fundamentals and implement embedded systems using ESP32, including sensor interfacing and actuator control. Develop connected IoT systems by enabling Wi-Fi communication and integrating cloud platforms for data storage, visualization, and remote device control. Process and analyze IoT data by performing data collection, cleaning, and feature engineering for real-world applications. Apply basic machine learning models (Decision Trees, KNN, Logistic Regression) to build predictive and classification systems using sensor data. Integrate AI with IoT systems to perform real-time predictions, automate decisions, and implement alert mechanisms. Design and develop end-to-end intelligent IoT solutions by combining hardware, connectivity, and AI concepts to solve real-world problems through practical projects.