I'm a data scientist, open-source Python developer, author and educator, with a passion for making machine learning practical and accessible.
I teach intermediate and advanced courses on machine learning, covering feature engineering, feature selection, hyperparameter tuning, imbalanced datasets, and the development of more effective machine learning pipelines.
I am the developer and maintainer of Feature-engine, an open-source Python library for feature engineering and feature selection. I am also the author of three books published by Packt: Python Feature Engineering Cookbook, Feature Selection in Machine Learning, and Imbalanced Data: Myths, Mistakes and Modern Solutions.
As a developer advocate, I enjoy connecting technical communities with the tools and knowledge they need to succeed. My work includes creating educational content, delivering talks and workshops, contributing to open-source software, and helping data scientists apply machine learning effectively in real-world projects.
I received a Data Science Leaders Award in 2018 and was recognized as one of LinkedIn’s voices in data science and analytics in 2019.
My academic background includes an MSc in Biology and a PhD in Biochemistry, followed by more than eight years as a research scientist at institutions including University College London and the Max Planck Institute.
