Supervised Learning in R - Classification Data Camp

Overview

Context

This beginner-level Supervised Learning in R - Classification course offered by Data Camp covers four of the most common classification algorithms. You will come away with a basic understanding of how each algorithm approaches a learning task, as well as learn the R functions needed to apply these tools to your own work.

What you will do during this course:

• As the kNN algorithm literally "learns by example" it is a case in point for starting to understand supervised machine learning. The first chapter will introduce classification while working through the application of kNN to self-driving vehicle road sign recognition
• Naive Bayes uses principles from the field of statistics to make predictions. The second chapter will introduce the basics of Bayesian methods while exploring how to apply these techniques to iPhone-like destination suggestions.
• Logistic regression involves fitting a curve to numeric data to make predictions about binary events. Arguably one of the most widely used machine learning methods, the third chapter will provide an overview of the technique while illustrating how to apply it to fundraising data.
• Classification trees use flowchart-like structures to make decisions. Because humans can readily understand these tree structures, classification trees are useful when transparency is needed, such as in loan approval. We'll use the Lending Club dataset to simulate this scenario.