Data Science with R: Decision Trees and Random Forests
INTERACTIVE COURSE

Data Science with R: Decision Trees and Random Forests

Intermediate

65 Lessons

14h

Certificate of Completion

AI Explanations
AI Explanations
Data Science with R: Decision Trees and Random Forests
67 Playgrounds
11 Quizzes
62 Illustrations

Takeaway Skills

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

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

Course Content

1

Welcome to the Course

2

Supervised Learning

3

Classification Tree Math

4

Using Classification Trees in R

5

Introducing the Bias-Variance Tradeoff

6

Model Tuning

4 Lessons

7

Model Tuning with tidymodels

5 Lessons

8

Feature Engineering

6 Lessons

9

Regression Trees

5 Lessons

10

The Random Forest Algorithm

4 Lessons

11

Using Random Forests

7 Lessons

12

Gradient Boosting Trees

6 Lessons

13

Continuing Your Journey

1 Lesson

COURSE AUTHOR

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