SVM for Beginners: Support Vector Machines in R Studio
Learn about creating Support Vector Machines in RStudio for use in machine learning in this free online course.
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This course provides you with in-depth knowledge and understanding of Support Vector Machines (SVM), giving the algorithms needed for machine learning and making powerful and highly accurate predictions based on available data. Such predictions’ accuracy depends on the SVM models’ efficacy, which is created and tuned to near-perfection. In this course, you will learn to create SVM models and determine where and how the models can be implemented. To follow this course, you will need to install R and RStudio on your computer. The method of installing the program Installation is demonstrated at the beginning of the course and you will be guided through this process. You will also be given a crash course in R and Rstudio, which will familiarise you with R commands, packages, data input methods, and the creation of bar plots and histograms.
Once you are equipped with your RStudio, you will be given a basic introduction to machine learning and the steps in building machine learning models. This guideline will help you understand the relation between machine learning and SVM. You will 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, the support vector classifier, to kernel-based support vector machines. The videos presented in this course will steer 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 will learn how to tune the hyperparameter for potentially better results. Next, you will build advanced SVM models with polynomial and radial kernels and their hyperparameter tuning. You will also learn 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. Support Vector Machines is the most preferred algorithm for machine learning because of its simplicity, accuracy, and usability in regression and classification tasks. Therefore, all aspiring machine-learning experts must have SVM in their arsenals. SVM is also valuable for scientific investigations, forensics, and disease diagnosis. This course will form a solid stepping stone in your career if you are looking to pursue a career in data analysis, data mining, or machine learning. On the other hand, if you are a professional looking to use machine learning in real-world business or science problems, this course is also for you. So, do not wait any longer. Jump into this course right away and become an expert in machine learning modelling in RStudio!Start Course Now
Setting up RStudio and R Crash Course
Setting up RStudio and R Crash Course - Learning Outcomes
R Installation and Basics
Plotting in R
Setting up RStudio and R Crash Course - Lesson Summary
Basics of Machine Learning and SVM
Basics of Machine Learning and SVM - Learning Outcomes
Machine Learning Basics
Maximum Margin Classifier
Support Vector Classifier
Basics of Machine Learning and SVM - Lesson Summary
Building SVM Models in R
Building SVM Models in R - Learning Outcomes
Classification SVM Model
Building SVM Models in R - Lesson Summary
After completing this module, you should be able to
- Outline the steps to install R and RStudio on any computer.
- Identify the sequence of steps to create a machine learning model
- Explain the concept of a hyperplane
- Distinguish between a Maximum Margin Classifier and a Support Vector Classifier
- Compare the different types of kernels
- Recall the steps to train a classification SVM model using linear, polynomial, and radial kernels
- Discuss the need for hyperparameter tuning in SVM
- Describe the process of training a regression SVM model
- Distinguish between the datasets used in classification and regression SVM models
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