Applied Python: Data, AI & Robotics

This unit builds on core Python programming by applying file I/O, modular design, error handling, and control structures to real-world data, machine learning, and robotics problems. Students will load and visualize datasets, develop basic TensorFlow models (locally, on GPU, and in the cloud), and interface Python code with sensors and actuators on Raspberry Pi and Grove-compatible hardware to prepare for an integrative capstone.

Learning Objectives

• Apply file input/output and JSON serialization/deserialization to load, parse, and persist datasets for analysis and model training
• Design and implement reusable Python modules, functions, and exception-handling strategies to produce robust, testable code
• Construct and evaluate simple TensorFlow neural network models, visualize results with Matplotlib, and compare execution options across local GPU and cloud environments
• Integrate and test Raspberry Pi/Grove sensors and actuators by writing Python code to drive servos/steppers and validate robot behavior using a RobotInterface