Train in Data | Data Science

Our Online Courses

Learn to transform, combine, extract and create features to use in machine learning models.

Learn to select the best variables to make simpler, yet robust and reliable machine learning models.

Learn to tune hyperparameters and improve the performance of your machine learning models.

Learn about re-sampling, ensemble and cost-sensitive learning and how to create synthetic data.

Learn the best practices and tools to deploy machine learning models and reduce deployment time.

Learn to test and monitor machine learning model deployments, including shadow mode.

Feature engineering and creation

Tools and classes for missing data imputation, categorical encoding, outlier trimming, discretization, feature creation and more.
Feature selection methods

A variety of methods to select the most relevant features in your data. Gathered from data science competitions and data mining articles.
Extensive docs and demos

Discover the power of Feature-engine through its extensive documentation, tutorials, user guides and demos.

70+ feature engineering recipes

Learn how to create, extract and transform features from tabular data, time series and text.
Open-source code implementations

Off-the-shelf feature engineering techniques with Scikit-learn, Feature-engine, Featuretools and more.
Work with different data sources

Extract, transform and create features from tabular, time series and text data.