Data Science with R: Decision Trees and Random Forests

This course introduces classification and regression trees (CART), random forest, and XGBoost machine learning algorithms using the R programming language.

Intermediate

65 Lessons

14h

Certificate of Completion

This course introduces classification and regression trees (CART), random forest, and XGBoost machine learning algorithms using the R programming language.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

67 Playgrounds

11 Quizzes

This course includes

67 Playgrounds

11 Quizzes

Course Overview

The R programming language is widely used in the field of data science. Machine learning is a fundamental skill for learners looking to master industry algorithms in the field of data science. In this course, you’ll learn about several essential algorithms used in machine learning, including classification and regression trees (CART), random forest, and XGBoost. CART is a decision tree algorithm that’s used for both classification and regression problems. Random forest is an ensemble learning method that u...Show More

TAKEAWAY SKILLS

Data Science

Machine Learning

Machine Learning Fundamentals

What You'll Learn

An understanding of the basics of machine learning and supervised learning

Familiarity with the differences between classification and regression trees

A working knowledge of XGBoost algorithm

Familiarity with random forest

What You'll Learn

An understanding of the basics of machine learning and supervised learning

Course Content

Welcome to the Course

Course Expectations

Machine Learning Is Predictive Analytics

The Course Datasets Quiz: Data Basics

Supervised Learning

What Is Machine Learning?

Forms of Supervised Learning Why Decision Trees?Decision Tree Intuition Overfitting Intuition Model Tuning Intuition Quiz: Machine Learning Concepts

Classification Tree Math

Gini Impurity Gini Change Classification Tree Training Example Many Categories Impurity Numeric Feature Impurity Quiz: Classification Tree Math

Using Classification Trees in R

Exploratory Data Analysis—Profiling Exploratory Data Analysis: Visualization Introducing tidymodels Crafting the Recipe Specify the Algorithm and Workflow Fitting the Model Quiz: Fitting Models

Introducing the Bias-Variance Tradeoff

Underfitting and Overfitting The Bias-Variance Tradeoff Quiz: Underfitting, Overfitting, and the Bias-Variance Tradeoff

Model Tuning

4 Lessons

Model Tuning with tidymodels

5 Lessons

Feature Engineering

6 Lessons

Regression Trees

5 Lessons

The Random Forest Algorithm

4 Lessons

Using Random Forests

7 Lessons

Gradient Boosting Trees

6 Lessons

Continuing Your Journey

1 Lesson

Course Author

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