Machine Learning with R

Introduction

This repository provides complete examples of machine learning algorithms using the statistical software R. Topics covered include the concepts and complete examples of machine learning, regression methods, classification, clustering, and neural networks.

The machine learning cheat sheet outlines machine learning concepts, R libraries, and mathematics. It is organized by machine learning tasks (supervised learning, unspervised learning, etc.).

All data used to train and test the machine learning algorithms in this package are stored in the 'data' folder, or are pulled from APIs, R packages, and web scraping techniques.

Topics

Supervised:

Regression Algorithms

• Simple Linear Regression
• Multiple Linear Regression
• Logistic Regression
• Regression Trees

Classification Algorithms

• Decision Tree Classifiers
• Naïve Bayes Classifiers

Unsupervised:

Neural Networks

• Artifical Neural Networks

Getting Started with R

You will need an integrated development environment (IDE) such as RStudio to execute R code.

It is recommended that you have a foundational understanding of both statistics and R to effectively use machine learning. The Statistics with R course on GitHub is a good place to start.