Book Image

Feature Engineering Made Easy

By : Sinan Ozdemir, Divya Susarla
Book Image

Feature Engineering Made Easy

By: Sinan Ozdemir, Divya Susarla

Overview of this book

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.
Table of Contents (14 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface

Case study 1 - facial recognition


Our first case study will be to predict the labels for image data with a popular dataset called the Labeled Faces in the Wild dataset from the scikit-learn library. The dataset is called the Olivetti Face dataset and it comprises pictures of famous people's faces, with appropriate labels. Our task is that of facial recognition, a supervised machine learning model that is able to predict the name of the person given an image of their face.

Applications of facial recognition

Image processing and facial recognition are far-reaching. The ability to quickly discern people's faces from a crowd of people in video/images is vital for physical security as well as for giant social media companies. Search engines such as Google, with their image search capabilities, are using image recognition algorithms to match images and quantify similarities to a point where we can upload a photo of someone to get all other images of that same person.

The data

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