<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-support-vector-machines-in-r/"></div></div><div></div></div><div></div></div>
<div>
(This article was first published on <strong><a href="https://www.datacamp.com/community/blog/course-support-vector-machines-r"> DataCamp Community - r programming</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><center><a rel="nofollow" href="https://www.datacamp.com/courses/support-vector-machines-in-r?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank"><img src="https://i1.wp.com/res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1530813251/support_vector_qmbymd.png?w=456" data-recalc-dims="1"></a></center></p>
<p>Here is the course <a rel="nofollow" href="https://www.datacamp.com/courses/support-vector-machines-in-r?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank">link</a>.</p>
<h2>Course Description</h2>
<p> Support Vector Machines in R will help students develop an understanding of the SVM model as a classifier and gain practical experience using R’s libsvm implementation from the e1071 package. Along the way, students will gain an intuitive understanding of important concepts, such as hard and soft margins, the kernel trick, different types of kernels, and how to tune SVM parameters. Get ready to classify data with this impressive model.</p>
<h2>Chapter 1: Introduction</h2>
<p><center><a rel="nofollow" href="https://www.datacamp.com/courses/support-vector-machines-in-r?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank"><img src="https://i0.wp.com/res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1530813236/04_-_linearly_separable_dataset_-_Yashas_Roy_l88eql.png?w=456" data-recalc-dims="1"></a></center></p>
<p>This chapter introduces some key concepts of support vector machines through a simple 1-dimensional example. Students are also walked through the creation of a linearly separable dataset that is used in the subsequent chapter.</p>
<h2>Chapter 2: Support Vector Classifiers – Linear Kernels</h2>
<p><center><a rel="nofollow" href="https://www.datacamp.com/courses/support-vector-machines-in-r?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank"><img src="https://i0.wp.com/res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1530813236/07-support_vector_vis_linear_svm_-_Yashas_Roy_r0cpcz.png?w=456" data-recalc-dims="1"></a></center></p>
<p>Chapter 2 introduces students to the basic concepts of support vector machines by applying the svm algorithm to a dataset that is linearly separable. Key concepts are illustrated through ggplot visualizations that are built from the outputs of the algorithm and the role of the cost parameter is highlighted via a simple example. The chapter closes with a section on how the algorithm deals with multi-class problems.</p>
<h2>Chapter 3: Polynomial Kernels</h2>
<p><center><a rel="nofollow" href="https://www.datacamp.com/courses/support-vector-machines-in-r?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank"><img src="https://i2.wp.com/res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1530813236/20_-_radially-separable-quadratic-default-plot_-_Yashas_Roy_xkf3in.png?w=456" data-recalc-dims="1"></a></center></p>
<p>This chapter provides an introduction to polynomial kernels via a dataset that is radially separable (i.e. has a circular decision boundary). After demonstrating the inadequacy of linear kernels for this dataset, students will see how a simple transformation renders the problem linearly separable thus motivating an intuitive discussion of the kernel trick. Students will then apply the polynomial kernel to the dataset and tune the resulting classifier.</p>
<h2>Chapter 4: Radial Basis Function Kernels</h2>
<p><center><a rel="nofollow" href="https://www.datacamp.com/courses/support-vector-machines-in-r?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank"><img src="https://i0.wp.com/res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1530813236/29_-_complex-radial-tuned_-_Yashas_Roy_jnrwtw.png?w=456" data-recalc-dims="1"></a></center></p>
<p>Chapter 4 builds on the previous three chapters by introducing the highly flexible Radial Basis Function (RBF) kernel. Students will create a &quot;complex&quot; dataset that shows up the limitations of polynomial kernels. Then, following an intuitive motivation for the RBF kernel, students see how it addresses the shortcomings of the other kernels discussed in this course.</p>
<p>The course requires the following prerequisites:</p>
<p><a rel="nofollow" href="https://www.datacamp.com/courses/free-introduction-to-r?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank">Introduction to R</a></p>
<p><a rel="nofollow" href="https://www.datacamp.com/courses/introduction-to-machine-learning-with-r?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank">Introduction to Machine Learning</a></p>
	<p>
<div>
<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="https://www.datacamp.com/community/blog/course-support-vector-machines-r"> DataCamp Community - r programming</a></strong>.</div>
<hr>
<a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers.com</a> offers <strong><a rel="nofollow" href="https://feedburner.google.com/fb/a/mailverify?uri=RBloggers">daily e-mail updates</a></strong> about <a rel="nofollow" href="https://www.r-project.org/" title="The R Project for Statistical Computing">R</a> news and <a rel="nofollow" href="https://www.r-bloggers.com/search/tutorial" title="R tutorials">tutorials</a> on topics such as: <a rel="nofollow" href="https://www.r-bloggers.com/search/data%20science" title="Data science">Data science</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Big%20Data" title="Big Data">Big Data, <a rel="nofollow" href="https://www.r-users.com/" title="R jobs">R jobs</a>, visualization (<a rel="nofollow" href="https://www.r-bloggers.com/search/ggplot2" title="ggplot and ggplot2 tutorials">ggplot2</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/boxplot" title="Boxplots using lattice and ggplot2 tutorials">Boxplots</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/map" title="Maps and gis">maps</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/animation" title="Animation in R">animation</a>), programming (<a rel="nofollow" href="https://www.r-bloggers.com/search/RStudio" title="RStudio IDE for R">RStudio</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/sweave" title="Sweave and literate programming">Sweave</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/LaTeX" title="LaTeX in R">LaTeX</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/SQL" title="SQL and databases">SQL</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/eclipse" title="Eclipse IDE for R">Eclipse</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/git" title="git and github, Version Control System">git</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/hadoop" title="Large data in R using Hadoop">hadoop</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Web+Scraping" title="Web Scraping of google, facebook, yahoo, twitter and more using R">Web Scraping</a>) statistics (<a rel="nofollow" href="https://www.r-bloggers.com/search/regression" title="Regressions and ANOVA analysis tutorials">regression</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/PCA" title="principal component analysis tutorial">PCA</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/time+series" title="Time series">time series</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/trading" title="finance trading">trading</a>) and more...
<img width="1" alt="" src="http://feeds.feedburner.com/~r/RBloggers/~4/rrAcytWwevw" height="1"></a></div>