Overview
A body of applied machine-learning work spanning regression, classification, NLP sentiment analysis, voice age prediction and reinforcement-learning experiments.
Selected experiments
- Regression & classification — supervised models with feature engineering, cross-validation and error analysis.
- NLP sentiment analysis — text preprocessing, embeddings and classifier evaluation.
- Voice age prediction — audio feature extraction and regression on voice characteristics.
- Computer vision — detection and tracking experiments that later fed into autonomous-systems work.
- Reinforcement learning — agents trained in simulated environments to explore control strategies.
Why it matters
These experiments are the foundation for the production AI work: they build the intuition for what models can and can't do, how to evaluate them honestly, and how to move from a notebook to something that runs reliably in an app.