▶Book Description
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 your 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 learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data.
By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization.
▶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
▶Key Features
- 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
▶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 the machine learning concepts and Python scripting would be enough to get started with this book.
▶What this book covers
- Chapter 1, Introduction to Feature Engineering, is an introduction to the basic terminology of feature engineering and a quick look at the types of problems we will be solving throughout this book.
- Chapter 2, Feature Understanding –. What's in My Dataset?, looks at the types of data we will encounter in the wild and how to deal with each one separately or together.
- Chapter 3, Feature Improvement - Cleaning Datasets, explains various ways to fill in missing data and how different techniques lead to different structural changes in data that may lead to poorer machine learning performance.
- Chapter 4, Feature Construction, is a look at how we can create new features based on what was already given to us in an effort to inflate the structure of data.
- Chapter 5, Feature Selection, shows quantitative measures to decide which features are worthy of being kept in our data pipeline.
- Chapter 6, Feature Transformations, uses advanced linear algebra and mathematical techniques to impose a rigid structure on data for the purpose of enhancing performance of our pipelines.
- Chapter 7, Feature Learning, covers the use of state-of-the-art machine learning and artificial intelligence learning algorithms to discover latent features of our data that few humans could fathom.
- Chapter 8, Case Studies, is an array of case studies shown in order to solidify the ideas of feature engineering.
