DataCamp
FreeSupport Vector Machines in R
4.8(91)11k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
This intermediate R course provides a thorough visual and practical introduction to Support Vector Machines (SVM). It demystifies key theoretical concepts such as margins and kernel tricks while teaching practical implementation with the e1071 package.
Who It's For
Data scientists and R analysts wanting an intuitive understanding of SVM classifiers, parameter tuning, and kernel selection.
Key Takeaways
- SVM Fundamentals: Master core concepts including hard vs. soft margins, hyperplanes, and classification boundary decisions.
- R Implementation: Use R's libsvm interface via the e1071 package for model fitting and classification.
- Kernel Selection & Tuning: Learn how to apply the kernel trick, compare kernel types, and tune SVM hyperparameters for optimal performance.
About this course
This course will introduce a powerful classifier, the support vector machine (SVM) using an intuitive, visual approach. 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.
Price
Free
This course is free to enrol.
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