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Data Science with R: Decision Trees and Random Forests

Name: Data Science with R: Decision Trees and Random Forests
Price: 59 USD
Author: David Langer

Gain insights into essential machine learning algorithms in R, including CART, random forest, and XGBoost. Discover model tuning and cross-validation to create accurate, robust data science models.

65 lessons

1 project

14h

Created by David Langer

Data Science with R: Decision Trees and Random Forests

What's included

65 lessons across 13 modules
11 quizzes with instant feedback
1 project in preconfigured environments
AI Tutor on every lesson

Join 3.1 million developers at

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

Learning Roadmap

65 Lessons11 Quizzes

Welcome to the Course

Get familiar with machine learning fundamentals, key datasets, and predictive analytics in R.

Course Expectations

Machine Learning Is Predictive Analytics

The Course Datasets

Quiz: Data Basics

Supervised Learning

Unpack the core of supervised learning, decision trees, overfitting, and model tuning in machine 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

6 Lessons

Examine key mathematical concepts and techniques for optimizing classification tree splits using Gini impurity.

Using Classification Trees in R

7 Lessons

Apply your skills to conduct EDA, prepare data, specify algorithms, and fit classification trees in R.

Introducing the Bias-Variance Tradeoff

3 Lessons

Take a look at understanding model complexity and the bias-variance tradeoff in machine learning.

Model Tuning

4 Lessons

Tackle cross-validation, optimal model tuning, decision tree complexity, and pruning to enhance model performance.

Model Tuning with tidymodels

5 Lessons

Piece together the parts of model accuracy, cross-validation, hyperparameter tuning, and visualization with tidymodels.

Feature Engineering

6 Lessons

Step through transforming raw data into meaningful features, avoiding information leakage, creating decision boundaries, and handling missing data.

Regression Trees

5 Lessons

Walk through regression trees, SSE, and training with tidymodels for numeric predictions.

10.

The Random Forest Algorithm

4 Lessons

Break apart the random forest's ensemble, bagging, and feature randomization techniques.

11.

Using Random Forests

7 Lessons

Break down the steps to train, tune, and evaluate random forest models using R.

12.

Gradient Boosting Trees

6 Lessons

Map out the steps for implementing and tuning Gradient Boosting Trees using XGBoost in R.

13.

Continuing Your Journey

Focus on hands-on practice, engaging in projects, and expanding skills to unsupervised learning.

What's Next?

Credit Card Fraud Detection using the R Language

Project

Premium

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE THIS COURSE

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 uses multiple decision trees to improve the accuracy of predictions. XGBoost, short for Extreme Gradient Boosting, is a powerful algorithm that’s also used for regression and classification problems. You’ll also learn about cross-validation and model tuning, which are essential skills for crafting valuable machine learning models. After taking this course, you’ll have the crucial skills to ensure that the machine learning models you create are accurate, robust, and reliable.

ABOUT THE AUTHOR

David Langer

I'm a hands-on analytics professional, having used my skills with Excel, SQL, and R/Python to craft insights, advise leaders, and shape company strategy. I'm also a skilled educator, having trained 1000s of working professionals over the years.

Learn more about David

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