Overview

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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. Independent Course Disclaimer Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.

Syllabus

Problem Scoping & Data Collection Pipeline

Frame the AQI prediction problem as a well-defined ML task, build automated data collection pipelines from multiple sources (APIs, web scraping, CSVs), store collected data in a structured database, and version all raw data for reproducibility.

Data Processing & Feature Engineering

Clean and validate messy multi-source data, engineer powerful features from temporal, weather, and geospatial signals, build a reproducible feature pipeline, and perform thorough exploratory analysis to guide model selection.

Model Building, Evaluation & Optimization

Train multiple ML models with proper cross-validation, systematically compare performance using appropriate metrics, optimize the best model via hyperparameter tuning, track all experiments with MLflow, and interpret model decisions for stakeholder communication.

Deployment, Monitoring & Production ML

Package and deploy the trained model as a production REST API, build an interactive monitoring dashboard, containerize the application with Docker, implement data/model drift detection, and set up automated retraining workflows.