Feature Engineering for Machine Learning (Techniques, Tools and Applications)
Feature Engineering for Machine Learning (Techniques, Tools and Applications)
Feature engineering is where machine learning success is truly built.
Algorithms get the spotlight, but the real competitive edge lies in how you shape your data. Feature Engineering for Machine Learning is your practical, no-nonsense guide to transforming raw data into powerful signals that unlock model performance, reliability, and insight.
Whether you're a data scientist, ML engineer, analyst, or student, this book takes you beyond theory into the craft of creating features that matter. You'll learn how to identify meaningful patterns, design robust transformations, handle messy real-world data, and systematically improve model outcomes.
Key Features
Through clear explanations, real examples, and actionable techniques, you'll discover how to:
- Turn raw data into predictive features
- Apply domain knowledge to engineer smarter signals
- Handle categorical, temporal, and high-dimensional data
- Avoid common pitfalls that sabotage model performance
- Build scalable, reproducible feature pipelines
Feature engineering isn’t just a step in the workflow, it's the foundation of great machine learning. Master it, and you don’t just build models… you build better decisions.
- Preface
- Acknowledgement
- INTRODUCTION TO FEATURE ENGINEERING
- BASIC FEATURE PREPROCESSING
- FEATURE ENGINEERING TECHNIQUES
- FEATURE EXTRACTION VS. FEATURE SELECTION
- ADVANCED TOPICS AND CASE STUDIES
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