Comprehensive guides on machine learning topics—from foundations to advanced methods
A comprehensive guide to causal inference—from potential outcomes and DAGs to advanced methods like DiD, IV, synthetic control, and causal machine learning.
From collaborative filtering basics to neural recommenders, graph-based methods, and causal recommendation—building personalized systems at scale.
Each series is designed as a structured learning path with theory, implementations, and practical applications. New series on deep learning, NLP, and reinforcement learning coming soon.