Hands-On Feature Engineering with Python [Video]

More Information
Learn
  • Master the insider tips for world-class feature engineering
  • Eliminate frustration and confusion in handling all aspects of features
  • Dramatically reduce the time required to move to the modelling steps of the process
  • Handle missing values with speed and ease
  • Systematically test for feature interaction terms build new features
  • Leverage advanced “target mean encoding” to maximize performance and understanding
  • Handle outliers automatically with much less effort
About

Feature engineering is the most important aspect of machine learning. You know that every day you put off learning the process, you are hurting your model’s performance. Studies repeatedly prove that feature engineering can be much more powerful than the choice of algorithms. Yet the field of feature engineering can seem overwhelming and confusing.

This course offers you the single best solution. In this course, all of the recommendations have been extensively tested and proven on real-world problems. You’ll find everything included: the recommendations, the code, the data sources, and the rationale. You’ll get an over-the-shoulder, step-by-step approach for every situation, and each segment can stand alone, allowing you to jump immediately to the topics most important to you.

By the end of the course, you’ll have a clear, concise path to feature engineering and will enable you to get improved results by applying feature engineering techniques on your datasets

All the code and supporting files for this course are available on GitHub at

https://github.com/PacktPublishing/Hands-On-Feature-Engineering-with-Python

Features
  • Get expert knowledge on different future engineering techniques on different datasets
  • Explore feature engineering techniques used in numerical datasets
  • Uncover and execute feature extraction popular and useful techniques
  • Build an ensemble model based on a feature engineered dataset
Course Length 1 hour 11 minutes
ISBN 9781789805567
Date Of Publication 26 Apr 2019