Feature Engineering for Machine Learning
Feature Engineering for Machine Learning Course
Learn imputation, variable encoding, discretization, feature extraction, how to work with datetime, outliers, and more.
Welcome to Feature Engineering for Machine Learning, the most comprehensive course on feature engineering available online. In this Feature Engineering for Machine Learning Course, you will learn about variable imputation, variable encoding, feature transformation, discretization, and how to create new features from your data. Master Feature Engineering and Feature Extraction. In this course, you will learn multiple feature engineering methods that will allow you to transform your data and leave it ready to train machine learning models.
The Most Comprehensive Online Course for Feature Engineering. There is no one single place to go to learn about feature engineering. It involves hours of searching on the web to find out what people are doing to get the most out of their data. This Feature Engineering for Machine Learning Course is taught by a lead data scientist with experience in the use of machine learning in finance and insurance, who is also a book author and the lead developer of a Python open source library for feature engineering.
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What you’ll learn
- Learn multiple techniques for missing data imputation.
- Transform categorical variables into numbers while capturing meaningful information.
- Learn how to deal with infrequent, rare, and unseen categories.
- Learn how to work with skewed variables.
- Convert numerical variables into discrete ones.
- Remove outliers from your variables.
- Extract useful features from dates and time variables.
- Learn techniques used in organizations worldwide and in data competitions.
- Increase your repertoire of techniques to preprocess data and build more powerful machine learning models.
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