A perfect guide to speed up the predicting power of machine learning algorithms
- Design, discover, and create dynamic, efficient features for your machine learning application
- Understand your data in depth and derive astonishing data insights with the help of this guide
- Grasp powerful feature engineering techniques and build machine learning systems
Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature engineering journey to make machine learning much more systematic and effective.
You will start with understanding your data; often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more. You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will also learn to convert a problem statement into useful new features. This book will guide you in delivering features driven by business needs as well as mathematical insights, and you'll see how to use machine learning for your data.
By the end of the book, you will have become proficient in feature selection, feature learning, and feature pptimization.
What you will learn
- Identify and leverage different feature types
- Clean features in data to improve predictive power
- Understand why and how to perform feature selection and model error analysis
- Leverage domain knowledge to construct new features
- Deliver features based on mathematical insights
- Use machine learning algorithms to construct features
- Master feature engineering and optimization
- Harness feature engineering for real-world applications through a structured case study
Who This Book Is For
If you are a data science professional or a machine learning engineer looking to strengthen your predictive analytics model, then this book is a perfect guide for you. Some basic understanding of machine learning concepts and Python scripting would be enough to get started with this book.
Table of Contents
- Introduction to Feature Engineering
- Feature Understanding - What's in My Data?
- Feature Improvement - Cleaning Datasets
- Feature Construction
- Feature Selection
- Feature Transformation - How to Change Your Perspective
- Feature Learning - Automatic Construction of Features
- Putting It All Together