Learn the complete machine learning lifecycle the way it actually happens in industry—through one cohesive, real-world project: building a real-time Urban Air Quality Index (AQI) prediction system. Starting from a blank repo, you'll scope the business problem, then collect data from government AQI APIs, OpenWeatherMap, and web-scraped traffic and industrial sources using scheduled, fault-tolerant ingestion scripts. You'll clean messy multi-source sensor data, engineer powerful temporal, weather, and geospatial features, and build a reproducible pipeline versioned with DVC. From there, you'll train and tune multiple models (Random Forest, XGBoost, LightGBM) with time-aware cross-validation, track every experiment in MLflow, and explain predictions with SHAP. Finally, you'll ship it: package the pipeline, serve it through a FastAPI REST endpoint, build an interactive map-based Streamlit dashboard, containerize with Docker, deploy to the cloud via CI/CD, and set up drift detection and automated retraining with Evidently AI. Across 4 modules and 42 focused videos, you'll finish with a production-grade, portfolio-ready ML system running end-to-end.

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