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Feature Engineering for Machine Learning


Learn how to engineer features and build more powerful machine learning models.

This is the most comprehensive, yet easy to follow, course for feature engineering available online. Throughout this course you will learn a variety of techniques used worldwide for data cleaning and feature transformation, gathered from data competition websites and white papers, blogs and forums, and from the instructor’s experience as a Data Scientist.

You will have at your fingertips, altogether in one place, a variety of techniques that you can apply to capture as much insight as you possibly can with the features of your data set.

The course starts describing the most simple and widely used methods for feature engineering, and then describes more advanced and innovative techniques that automatically capture insight from your variables. It includes an explanation of the feature engineering technique, the rationale to use it, and the pros, cons and assumptions it makes. It also includes full code that you can then take on and apply to your own data sets.

This course is therefore suitable for complete beginners in data science looking to learn their first steps into data pre-processing, as well as for intermediate and even advanced data scientists seeking to level up their skills.

With more than 50 lectures and 10 hours of video this comprehensive course covers every aspect of variable transformation. Throughout the course we use python as our main language.

This course comes with a 30 day money back guarantee. In the unlikely event you don’t find this course useful, you’ll get your money back, no questions asked.

Who this course is for:

  • Beginner Data Scientists who want to get started in pre-processing datasets to build machine learning models
  • Intermediate Data Scientists who want to level up their experience in feature engineering for machine learning
  • Advanced Data Scientists who want to discover new and innovative techniques for feature engineering
  • Software engineers, mathematicians and academics switching careers into data science
  • Software engineers, mathematicians and academics stepping into data science


  • A Python installation
  • Jupyter notebook installation
  • Python coding skills
  • Some experience with Numpy and Pandas
  • Familiarity with Machine Learning algorithms
  • Familiarity with scikit-learn

Last updated 2/2019


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