ananttripathi/AI-ML-Projects-UT-Austin

Covers 11 end-to-end projects spanning classification, time series forecasting, computer vision, and generative AI—each structured with business problem definition, exploratory analysis, modeling, and deployment concepts. Projects employ diverse ML paradigms including cost-sensitive learning for industrial maintenance, agentic workflows for information retrieval, retrieval-augmented generation (RAG) for medical knowledge systems, and MLOps pipelines with CI/CD automation. Notebooks run in Jupyter/Colab with modular folder organization, individual requirements files, and business-focused insights aligned to real-world use cases across finance, retail, healthcare, and manufacturing domains.