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Erin LeDell
Erin LeDell

Chief Machine Learning Scientist at H2O.ai

Dr. Erin LeDell is a Machine Learning Scientist at H2O.ai. 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 H2O.ai, 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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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 rladies.org.

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Collaborator(s)
  • Nick Carchedi

    Nick Carchedi

  • Nick Solomon

    Nick Solomon

Prerequisites

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 classification 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

    Free

    This chapter covers supervised machine learning with classification trees.

  2. Regression Trees

    In this chapter you'll learn how to use a single tree for regression, instead of classification.

  3. Bagged Trees

    In this chapter, you will learn about Bagged Trees, an ensemble method, that uses a combination of trees (instead of only one).

  4. Random Forests

    In this chapter, you will learn about the Random Forest algorithm, another tree-based ensemble method. Random Forest is a modified version of bagged trees with better performance. Here you'll learn how to train, tune and evaluate Random Forest models in R.

  5. Boosted Trees

    In this chapter, you will see the boosting methodology with a focus on the Gradient Boosting Machine (GBM) algorithm, another popular tree-based ensemble method. Here you'll learn how to train, tune and evaluate GBM models in R.

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