Feature Engineering & Selection for Explainable Models
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Feature Engineering & Selection for Explainable Models

A Second Course for Data Scientists

About the Book

Learn about the 10 percent of things that make 90 percent of impact in your machine learning projects.

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About the Author

Md Azimul Haque
Md Azimul Haque

I am an experienced data scientist and manager with a long track record of delivering machine learning projects.

It is easy to learn about data science with the help of courses. It is also easier to crack data science interviews with the proper preparation. Succeeding in your data science and machine learning job is another endeavor altogether. Many times in real-world machine learning projects, model performance gets stuck and cannot move beyond the low-performing benchmark model.

This book Feature Engineering & Selection for Explainable Models A Second Course for Data Scientists bridges the gap between what is needed on the job and what is taught in most online and offline data science courses. It talks about the few skills that bring 90 percent of impact in your machine learning project. It addresses the most common issues that can change the outcome of a machine-learning project: feature engineering, feature selection, and model explanation — The interplay between these 3 methods.

Table of Contents

  • Chapter 1: Introduction
  • Chapter 2: Domain Specific Feature Engineering
  • Chapter 3: EDA Feature Engineering
  • Chapter 4: Higher Order Feature Engineering
  • Chapter 5: Interaction Effect Feature Engineering
  • Chapter 6: Fundamentals of Feature Selection
  • Chapter 7: Feature Selection Concerning Modeling Techniques
  • Chapter 8: Feature Selection Using Metaheuristic Algorithms
  • Chapter 9: Explaining Model and Model Predictions to Layman
  • Chapter 10: Feature Engineering & Selection for Text Classification
  • Chapter 11: Things That Can Give Additional Improvement
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