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

Appendix 1. Other Books You May Enjoy

If you enjoyed this book, you may be interested in these other books by Packt:

Statistics for Machine Learning Pratap Dangeti

ISBN: 978-1-78829-575-8

  • Learn how to clean your data and ready it for analysis
  • Understand the Statistical and Machine Learning fundamentals necessary to build models
  • Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems
  • Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages
  • Analyze the results and tune the model appropriately to your own predictive goals
  • Understand the concepts of required statistics for Machine Learning
  • Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models
  • Learn reinforcement learning and its application in the field of artificial intelligence domain

Machine Learning Algorithms Giuseppe Bonaccorso

ISBN: 978-1-78588-962-2

  • Acquaint yourself with important elements of Machine Learning
  • Understand the feature selection and feature engineering process
  • Assess performance and error trade-offs for Linear Regression
  • Build a data model and understand how it works by using different types of algorithm
  • Learn to tune the parameters of Support Vector machines
  • Implement clusters to a dataset
  • Explore the concept of Natural Processing Language and Recommendation Systems
  • Create a ML architecture from scratch