Mastering Tabular ML: Feature Engineering to Production

About this course

Master the art and science of building high-performance models for tabular data—the most common data format in industry—through one cohesive, real-world project: a Dynamic Pricing Engine for a ride-hailing platform that predicts trip fares and surge multipliers. You'll start with rigorous EDA and leakage-proof validation, then engineer 150+ high-signal features from numerical, categorical, datetime, and geospatial columns, including target encoding, Haversine distances, and automated feature synthesis with Featuretools. From there, you'll go deep into the engines that dominate tabular ML: master XGBoost internals (regularization, sparsity-aware splits, monotonic constraints) and LightGBM internals (leaf-wise growth, GOSS, EFB, native categorical handling, GPU training), then benchmark them head-to-head alongside CatBoost. Finally, you'll tune with Optuna, select features with SHAP and Boruta, build multi-layer stacking ensembles, and deploy the pricing engine as a production FastAPI endpoint. Following the Kaggle Grandmasters' playbook—large-scale feature generation, stacking, and adversarial validation—you'll finish with a production-ready, portfolio-grade pricing system across 4 modules and 36 focused videos. 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.