Data Science

Just launched!

Interpreting Machine Learning Models

Find out more about our new course (if the video does not show, try allowing cookies in your browser).

Our courses

Feature Engineering for Machine Learning

Learn imputation, variable encoding, discretization, feature extraction, how to work with datetime, outliers, and more.

Soledad Galli

$37.99

Feature Selection for Machine Learning

Learn filter, wrapper, and embedded methods, recursive feature elimination, exhaustive search, feature shuffling & more.

Soledad Galli

$33.99

Hyperparameter Optimization for Machine Learning

Learn grid and random search, Bayesian optimization, multi-fidelity models, Optuna, Hyperopt, Scikit-Optimize & more.

Soledad Galli

$35.99

Machine Learning with Imbalanced Data

Learn to over-sample and under-sample your data, apply SMOTE, ensemble methods, and cost-sensitive learning.

Soledad Galli

$33.99

Feature Engineering for Time Series Forecasting

Create lag, window and seasonal features, perform imputation and encoding, extract datetime variables, remove outliers, and more.

Kishan Manani

$39.99

Machine Learning Interpretability

Explain interpretable and black box models with LIME, Shap, partial dependency plots and more.

Soledad Galli

$37.99

Our books

70 Python Feature Engineering Recipes for Machine Learning

Python Feature engineering Cookbook

20 Python Feature Slection Recipes for Machine Learning

Feature Selection in Machine Learning

We're the developers of Feature-engine

Feature-engine: Python Open Source Feature Engineering Library

► 150k+ downloads a month

► 1.5k stars on Github

► 40+ contributors

►Feature engineering and selection

Our Instructors

Soledad Galli: Data Scientist, Instructor, Open Source Developer

Soledad Galli, PhD

Lead Data Scientist

Kishan Manani, Data Scientist, Instructor

Accelerate Your Data Science Career

With Specialized Machine Learning Courses

Just launched!

Interpreting Machine Learning Models

Our courses

Feature Engineering for Machine Learning

Learn imputation, variable encoding, discretization, feature extraction, how to work with datetime, outliers, and more.

Feature Selection for Machine Learning

Learn filter, wrapper, and embedded methods, recursive feature elimination, exhaustive search, feature shuffling & more.

Hyperparameter Optimization for Machine Learning

Learn grid and random search, Bayesian optimization, multi-fidelity models, Optuna, Hyperopt, Scikit-Optimize & more.

Machine Learning with Imbalanced Data

Learn to over-sample and under-sample your data, apply SMOTE, ensemble methods, and cost-sensitive learning.

Feature Engineering for Time Series Forecasting

Create lag, window and seasonal features, perform imputation and encoding, extract datetime variables, remove outliers, and more.

Machine Learning Interpretability

Explain interpretable and black box models with LIME, Shap, partial dependency plots and more.

Our books

Python Feature engineering Cookbook

Feature Selection in Machine Learning

We're the developers of Feature-engine

► 150k+ downloads a month

► 1.5k stars on Github

► 40+ contributors

►Feature engineering and selection

Our Instructors

Soledad Galli, PhD

Lead Data Scientist

Kishan Manani, PhD

Lead Data Scientist

Proud Sponsors of the

AI Stories Podcast

Listen to Top Data Scientists sharing their path to success, work lives and vision.

Ready to take your skills to the next level?

Follow us 👇

Accelerate Your Data Science Career

With Specialized Machine Learning Courses

Just launched!

Interpreting Machine Learning Models

Our courses

Feature Engineering for Machine Learning

Learn imputation, variable encoding, discretization, feature extraction, how to work with datetime, outliers, and more.

Feature Selection for Machine Learning

Learn filter, wrapper, and embedded methods, recursive feature elimination, exhaustive search, feature shuffling &amp; more.

Hyperparameter Optimization for Machine Learning

Learn grid and random search, Bayesian optimization, multi-fidelity models, Optuna, Hyperopt, Scikit-Optimize &amp; more.

Machine Learning with Imbalanced Data

Learn to over-sample and under-sample your data, apply SMOTE, ensemble methods, and cost-sensitive learning.

Feature Engineering for Time Series Forecasting

Create lag, window and seasonal features, perform imputation and encoding, extract datetime variables, remove outliers, and more.

Machine Learning Interpretability

Explain interpretable and black box models with LIME, Shap, partial dependency plots and more.

Our books

Python Feature engineering Cookbook

Feature Selection in Machine Learning

We're the developers of Feature-engine

► 150k+ downloads a month

► 1.5k stars on Github

► 40+ contributors

►Feature engineering and selection

Our Instructors

Soledad Galli, PhD

Lead Data Scientist

Kishan Manani, PhD

Lead Data Scientist

Proud Sponsors of the

AI Stories Podcast

Listen to Top Data Scientists sharing their path to success, work lives and vision.

Ready to take your skills to the next level?

Follow us 👇

Learn filter, wrapper, and embedded methods, recursive feature elimination, exhaustive search, feature shuffling & more.

Learn grid and random search, Bayesian optimization, multi-fidelity models, Optuna, Hyperopt, Scikit-Optimize & more.