Gabriela de Queiroz
Gabriela de Queiroz

Data Scientist and founder of R-Ladies

Gabriela de Queiroz is a Data Scientist and the founder of R-Ladies, a world-wide organization for promoting diversity in the R community. She likes to mentor and share her knowledge through mentorship programs, tutorials and talks. She has worked in several startups and where she built teams, developed statistical models and employed a variety of techniques to derive insights and drive data-centric decisions. She holds 2 Master’s: one in Epidemiology and one in Statistics. Follow her at @gdequeiroz on Twitter and find out more about R-Ladies at

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Erin LeDell
Erin LeDell

Machine Learning Scientist at and co-author of the h2o package

Dr. Erin LeDell is a Machine Learning Scientist at She is the co-author of several R packages, including the h2o package for machine learning. She is the founder of the Women in Machine Learning & Data Science organization and is a member of the R-Ladies Global Leadership team. Before working at, she worked as a data scientist, founded DataScientific, Inc and received a PhD in Biostatistics from UC Berkeley. Follow @ledell on Twitter.

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  • Vincent Vankrunkelsven

    Vincent Vankrunkelsven

  • Nick Carchedi

    Nick Carchedi

  • Nick Solomon

    Nick Solomon

Course Description

In this course you'll learn how to work with tree-based models in R. This course covers everything from using a single tree for regression or classififcation to more advanced ensemble methods. You'll learn to implement bagged trees, Random Forests, and boosted trees using the Gradient Boosting Machine, or GBM. These powerful techinques will allow you to create high performance regression and classification models for your data.

  1. 1

    Classification Trees

  2. Regression Trees

  3. Bagged Trees

  4. Random Forests

  5. Boosted Trees