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SVM for Beginners: Support Vector Machines in R Studio

Explore support vector machines in RStudio for use in machine learning in this free online data science course.

Publisher: Start-Tech Academy
This free online course teaches you all you need to know about support vector machines (SVM) and their role in machine learning. We explain how to create both classification and regression SVM models using the ‘R’ program in the RStudio environment. These include simple to advanced models that use linear and non-linear kernels. This course can help you to solve problems and predict future changes using machine learning.
SVM for Beginners: Support Vector Machines in R Studio
  • Duration

    4-5 Hours
  • Students

  • Accreditation


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This course provides you with in-depth knowledge and understanding of support vector machines (SVM), unpacking the algorithms needed to utilize machine learning to make highly accurate predictions based on available data. Such predictions’ accuracy depends on the SVM models’ efficacy, which is created and tuned to near-perfection. We demonstrate how to create SVM models and determine where and how the models can be implemented. To follow this course, you should install R and RStudio on your computer and we show you how. We then take you through a crash course in R and Rstudio, which familiarizes you with R commands, packages, data input methods and the creation of bar plots and histograms.

Once you are equipped with RStudio, you receive a basic introduction to machine learning and the steps employed in building such models. This will help you understand the relation between machine learning and SVM. We then begin your SVM model-building journey with the concept of ‘hyperplanes’ and ‘maximum margin classifier’. The limitations of this classifier will lead you, through another type of classifier called the ‘support vector classifier’, to kernel-based support vector machines. This course guides you through the steps of data-tidying and preprocessing, loading the data into the R environment, defining the SVM classification model and training the model of the data. Once you have built a basic SVM model with a linear kernel, you can tune the hyperparameter to improve results or build advanced SVM models with polynomial and radial kernels. We also explain how to build SVM-based regression models in R.

Is the idea of machine learning fascinating to you? Do you find the notion of analytical model building as a method of data analysis intriguing? Find out how machine learning has become an essential tool in business analytics and intelligence with this course. Support vector machines are the most preferred algorithm for machine learning because of their simplicity, accuracy and usability in regression and classification tasks. Therefore, all aspiring machine-learning experts must have SVM in their arsenals as it aids scientific investigations, forensics and disease diagnosis. This course provides a solid stepping stone in your career if you are looking to pursue data analysis, data mining or machine learning. We can also help any professional looking to use such learning in real-world business or science problems.

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