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  • 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

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.

  • 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
Page Count 316
Course Length 9 hours 28 minutes
ISBN 9781787287600
Date Of Publication 22 Jan 2018


Sinan Ozdemir

Sinan Ozdemir is a data scientist, start-up founder, and educator living in the San Francisco Bay Area. He studied pure mathematics at the Johns Hopkins University. He then spent several years conducting lectures on data science there, before founding his own start-up, Kylie ai, which uses artificial intelligence to clone brand personalities and automate customer service communications. He is also the author of Principles of Data Science, available through Packt.

Divya Susarla

Divya Susarla is an experienced leader in data methods, implementing and applying tactics across a range of industries and fields including investment management, social enterprise consulting, and wine marketing. She trained in data by way of specializing in Economics and Political Science at University of California, Irvine, cultivating a passion for teaching by developing an analytically based, international affairs curriculum for students through the Global Connect program.

Divya is currently focused on natural language processing and generation techniques at, a startup helping clients automate their customer support conversations. When she is not busy working on building and writing educational content, she spends her time traveling across the globe and experimenting with new recipes at her home in Berkeley, CA.