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Hands-On Feature Engineering with Python [Video]

More Information
  • 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 modeling 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

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 own datasets

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

Style and Approach

This course is a hands-on guide filled with practical tutorials and real-world datasets. It takes a step-by-step approach where viewers will get an idea of when to apply which type of feature engineering to get the most accurate results for machine learning applications.

  • Get expert knowledge of feature engineering techniques for different datasets such as videos, text, images, and audio samples
  • Uncover and execute feature extraction with detailed deep-learning techniques
  • Discover how to perform feature engineering on unsupervised learning and semi-supervised learning
Course Length 1 hour 11 minutes
ISBN 9781789805567
Date Of Publication 24 Apr 2019


Sahiba Chopra

Sahiba Chopra has a Bachelors degree in economics and Chinese. She has 2.4 years of experience working in data science projects in renewable energy, video streaming, and microfinance. Currently, she's working as a Lead Data Scientist at HAPPY - Financial Services. She regularly writes blogs on data science. You can follow her on LinkedIn https://www.linkedin.com/in/sahiba-chopra-3762124a