<p><div><div><div><div data-show-faces="false" data-height="21" data-width="100" data-layout="button_count" data-send="true" data-href="https://www.r-bloggers.com/new-course-supervised-learning-in-r-classification/"></div></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="http://www.datacamp.com/community/blog/new-course-supervised-learning-in-r-classification"> DataCamp Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Hi there! We proud to launch our latest R &amp; machine learning course, <a rel="nofollow" href="https://www.datacamp.com/courses/supervised-learning-in-r-classification?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank">Supervised Learning in R: Classification</a>! By <a rel="nofollow" href="https://www.datacamp.com/instructors/lantzb?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank">Brett Lantz</a>.</p>
<p>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.</p>
<p><center><strong><a rel="nofollow" href="https://campus.datacamp.com/courses/supervised-learning-in-r-classification/chapter-1-k-nearest-neighbors-knn?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank">Take me to chapter 1!</a></strong></center></p>
<p>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!</p>

<p><a rel="nofollow" href="https://www.datacamp.com/courses/supervised-learning-in-r-classification?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank"><img alt="" src="https://i1.wp.com/community.datacamp.com.s3.amazonaws.com/community/production/ckeditor_assets/pictures/722/content_1200x628_new_course_3.png?w=456" data-recalc-dims="1"></a></p>

<p><strong>What you’ll learn:</strong></p>
<p><strong>Chapter 1: k-Nearest Neighbors (kNN)</strong></p>
<p>This chapter will introduce classification while working through the application of kNN to self-driving vehicles.</p>
<p><strong>Chapter 2: Naive Bayes</strong></p>
<p>Naive Bayes uses principles from the field of statistics to make predictions. This chapter will introduce the basics of Bayesian methods.</p>
<p><strong>Chapter 3: Logistic Regression</strong></p>
<p>Logistic regression involved fitting a curve to numeric data to make predictions about binary events.</p>
<p><strong>Chapter 4: Classification Trees</strong></p>
<p>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.</p>

<p><center><a rel="nofollow" href="https://www.datacamp.com/courses/supervised-learning-in-r-classificationhttps://www.datacamp.com/courses/supervised-learning-in-r-classification?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank"><strong>Start your path to mastering ML in R with Supervised Learning in R: Classification!</strong></a></center></p>
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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://www.datacamp.com/community/blog/new-course-supervised-learning-in-r-classification"> DataCamp Blog</a></strong>.</div>
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