Course Machine Learning with Tree-Based Models in R

In this course, you’ll learn how to use tree-based models and ensembles for regression and classification.

DescriptionChaptersExercisesInstructor

This online course about Machine Learning with Tree-Based Models in R covers a key part of what a future data analyst would require.

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.

Enroll now in this Machine Learning with Tree-Based Models in R course, and don’t miss the opportunity of learning with the best, as Erin LeDell is. With 58 enriching exercises, 20 videos, and an estimated time of 4 hours to successfully end up the course, you will become one of the best.

Requisites before you start
Chapter 1: Classification Trees
This chapter covers supervised machine learning with classification trees.
Chapter 2: Bagged Trees
In this chapter, you will learn about Bagged Trees, an ensemble method, that uses a combination of trees (instead of only one).
Chapter 3: 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.
Chapter 4: Regression Trees
In this chapter you’ll learn how to use a single tree for regression, instead of classification.
Chapter 5: 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.
Chapter 6: Classification Trees
This chapter covers supervised machine learning with classification trees.
Chapter 7: Regression Trees
In this chapter you’ll learn how to use a single tree for regression, instead of classification.
Chapter 8: Bagged Trees
In this chapter, you will learn about Bagged Trees, an ensemble method, that uses a combination of trees (instead of only one).
Chapter 9: 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.
Chapter 10: 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.
Machine Learning with Tree-Based Models in R. In this course, you'll learn how to use tree-based models and ensembles for regression and classification.

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.

Collaborators