Data Science

Just launched

Feature engineering for forecasting

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

$19.99

Feature Selection for Machine Learning

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

Soledad Galli

$19.99

Hyperparameter Optimization for Machine Learning

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

Soledad Galli

$19.99

Machine Learning with Imbalanced Data

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

Soledad Galli

$19.99

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

$24.99

☻ More than 42k students enrolled.

☻ Almost 10k student reviews.

☻ Average courses rating: 4.6 out of 5.

Students, reviews and rating of our courses

Our Python Open-Source

Feature-engine, a python open source library for feature engineering

➊ Transformers for feature engineering and creation.

➋ Algorithms for feature selection.

➌ Extensive docs and demos.

Our books

Feature engineering for machine learning

Feature selection in machine learning with Python

Our Instructors

Soledad Galli, data scientist, instructor, open source developer

Soledad Galli, PhD

Lead Data Scientist

Kishan Manani, data scientist, instructor

Intermediate and advanced courses in data science

Learn best practices for machine learning and AI software engineering from experienced professionals.

Just launched

Feature engineering for forecasting

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.

☻ More than 42k students enrolled.

☻ Almost 10k student reviews.

☻ Average courses rating: 4.6 out of 5.

Our Python Open-Source

➊ Transformers for feature engineering and creation.

➋ Algorithms for feature selection.

➌ Extensive docs and demos.

Our books

Feature engineering for machine learning

Feature selection in machine learning with Python

Our Instructors

Soledad Galli, PhD

Lead Data Scientist

Kishan Manani, PhD

Lead Data Scientist

Subscribe

Intermediate and advanced courses in data science

Learn best practices for machine learning and AI software engineering from experienced professionals.

Just launched

Feature engineering for forecasting

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.

☻ More than 42k students enrolled.

☻ Almost 10k student reviews.

☻ Average courses rating: 4.6 out of 5.

Our Python Open-Source

➊ Transformers for feature engineering and creation.

➋ Algorithms for feature selection.

➌ Extensive docs and demos.

Our books

Feature engineering for machine learning

Feature selection in machine learning with Python

Our Instructors

Soledad Galli, PhD

Lead Data Scientist

Kishan Manani, PhD

Lead Data Scientist

Subscribe

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.