May 2025 monthly summary for kietmcaproject/AI_AI101B_2024-25: Key feature delivered is the Multimodal Sentiment Analysis Script (CLIP), implemented in Python to analyze both images and text prompts and predict sentiment (positive, neutral, negative). This demonstrates cross-modal sentiment alignment and enables downstream business insights from multimedia content. No major bugs fixed documented for this period in the provided scope. Overall impact includes expanded sentiment analysis capabilities across visual and textual modalities, enabling data-driven decisions for content moderation, user feedback interpretation, and marketing optimization. Technologies and skills demonstrated include Python scripting, CLIP-based multimodal inference, image/text data handling, and version-controlled development with clear commit traceability.

April 2025 — Delivered end-to-end features in kietmcaproject/AI_AI101B_2024-25, focusing on practical AI/ML workflows and systems simulation. Two main features were completed: Traffic Light Control System Simulation (Python, TrafficLight class, NS/EW state transitions, timed cycles) with accompanying AI report and presentation materials; and a TensorFlow/Keras handwritten digit recognition workflow (Jupyter notebook) covering data preprocessing, model training with Adam, evaluation, and prediction visualization. No major bugs fixed in this period. Impact: provides a reproducible foundation for policy testing and ML learning, enabling stakeholders to explore traffic-control scenarios and digit recognition pipelines. Demonstrates strong Python software engineering, OOP, state-machine design, and ML engineering skills (TensorFlow/Keras, Jupyter), delivering business value through ready-to-run experiments and educational materials.

Month: 2024-12 — Key achievements delivered a formal project deliverable and cleaning of placeholder artifacts to maintain a clean, production-ready repository state.