Feature Engineering & Selection for Explainable Models: A Second Course for Data Scientists (Revised Edition)

Azimul is an IEEE senior member, data scientist, researcher, and educator focused on one core mission: turning complex AI ideas into practical, trustworthy systems. With more than a decade of hands-on experience in machine learning and advanced analytics across industries such as market research, marketing, hospitality, healthcare, and oil & gas, he has built models that don’t just work in theory, but deliver measurable impact in the real world.

His work sits at the intersection of feature engineering, explainable AI, and neuro-symbolic systems. Drawing on deep expertise in metaheuristic algorithms, signal processing, and model interpretability, Azimul develops methods that help models both perform and explain themselves. He has contributed to open-source projects in feature engineering and XAI, reflecting his belief that powerful tools should be accessible to practitioners everywhere.

Azimul is the author of Feature Engineering & Selection for Explainable Models: A Second Course for Data Scientists (Revised Edition), a practical guide to building transparent, high-performing models, and Neuro-Symbolic AI: Fusing Neural Networks and Reasoning for Accelerating Artificial General Intelligence, which explores how to combine deep learning with logical reasoning to move beyond black-box AI.

Whether he’s writing, teaching, or building systems, Azimul is driven by a simple idea: AI should see, think, and explain, and every practitioner should have the tools to make that happen.

Stay updated on his work, upcoming releases, and practical machine learning insights by following his Amazon Author Page.