Feature Engineering & Selection for Explainable Models
A Second Course for Data Scientists (Revised Edition)
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About
About the Book
Master the Art of Feature Engineering and Selection to Unlock the True Mark of a Great Data Scientist - Bridge the Gap Between Raw Data and Actionable Insights With This Book That Shows You How!
Struggling to build machine learning models that truly deliver?
Ready to go beyond basic techniques and make your models explainable, impactful, and job-ready?
This is the course your career needs.
In the age of black-box algorithms, feature engineering and selection is your secret weapon to build models that not only perform better but also explain why they work. This book takes you beyond the basics, diving deep into the real-world challenges of machine learning. With practical guidance, cutting-edge algorithms, and fully developed Python libraries, this is the only resource you’ll need to elevate your data science game.
Whether you’re a budding data scientist, an experienced professional, or a student aiming for the top, this book delivers tailored benefits:
● Build Better Models: Learn metaheuristic algorithms like genetic algorithms, simulated annealing, particle swarm optimization, and ant colony optimization to perform feature selection with precision.
● Leverage Open-Source Tools: Master the art of feature engineering and signal processing—all in Python—with open-source libraries developed exclusively for this book.
● Solve Real Problems: Go beyond toy datasets and experience the full journey of machine learning model development with four real-world datasets, tackling challenges every data scientist faces.
● Communicate Results Clearly: Learn to justify outcomes with data and facts, even when models fail to meet expectations.
● Future-Proof Your Skills: Gain practical expertise to deliver models that excel in both performance and explainability, setting yourself apart in the competitive world of AI.
● And more!
Ready to become the data scientist who builds explainable models that succeed in the real world?
Get your copy of "Feature Engineering & Selection for Explainable Models" and take your skills to the next level today!
Scroll up, Click on “Buy Now”, and Grab Your Copy Today!
Author
About the Author
Md Azimul Haque
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.
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Also by the Author
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