Applied Python: Data, AI, and Robotics

This unit integrates intermediate Python programming with data handling, machine learning, visualization, and embedded hardware control. Students will consolidate file and JSON I/O, exception handling, modular code design, basic TensorFlow model development and visualization, and practical sensor/actuator projects to prepare for advanced networking and AI applications.

Learning Objectives

• Analyze and handle runtime errors by using specific exception types and implementing error-handling strategies that prevent application crashes and support graceful recovery
• Apply file input/output and data serialization techniques to read, iterate, parse, and write plain text and JSON files for working with medium-sized datasets
• Demonstrate the development, training, and evaluation of basic machine learning models using TensorFlow (locally, on GPU, and in cloud environments) and create data and model visualizations with Matplotlib in Jupyter Notebooks
• Implement modular Python code and hardware interfacing by building, testing, and documenting sensor and actuator projects (Raspberry Pi, Grove, servos, and stepper motors), including designing reusable modules and classes for robot control