new machine learning course!

Learn four of the most common classification algorithms in this beginner-level introduction to machine learning. 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. [DataCamp]( New Machine Learning Course! [ ]( [Play Now]( [ ]( Supervised Learning in R: Classification Taught by Brett Lantz, Data Scientist at the University of Michigan This introduction to machine learning covers four of the most common classification algorithms. You will learn how each algorithm approaches a learning task, as well as the R functions needed to apply these tools to your own work. [Start Learning]( Supervised Learning in R: Classification: 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 involves 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 can readily understand these tree structures, classification trees are useful when transparency is needed. [Play Now]( [DataCamp] [DataCamp]( DataCamp Inc. | 2067 Massachusetts Avenue | Cambridge MA 02140 [Facebook] [Facebook]( [Twitter] [Twitter]( [LinkedIn] [LinkedIn]( [YouTube] [YouTube]( [Unsubscribe](