Hi there! We proud to launch our latest R & machine learning course, Supervised Learning in R: Classification! By Brett Lantz.

This beginner-level introduction to machine learning 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.

Supervised Learning in R: Classification features interactive exercises that combine high-quality video, in-browser coding, and gamification for an engaging learning experience that will make you an expert in machine learning in R!

What you’ll learn:

Chapter 1: k-Nearest Neighbors (kNN)

This chapter will introduce classification while working through the application of kNN to self-driving vehicles.

Chapter 2: Naive Bayes

Naive Bayes uses principles from the field of statistics to make predictions. This chapter will introduce the basics of Bayesian methods.

Chapter 3: Logistic Regression

Logistic regression involved fitting a curve to numeric data to make predictions about binary events.

Chapter 4: Classification Trees

Classification trees use flowchart-like structures to make decisions. Because humans an readily understand these tree structures, classification trees are useful when transparency is needed.