Learn Support Vector Machines in R Studio. Basic SVM models to kernel-based advanced SVM models of Machine Learning
InstructorStart-Tech Academy
Free
Free
The curriculum includes a dedicated appendix covering data exploration, outlier treatment, missing value imputation, and variable transformation.
Instruction covers the application of linear, polynomial, and radial kernels, including hyperparameter tuning within R Studio.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This slide introduces the course instructors, Abhishek Bansal and Pukhraj Parikh, detailing their educational backgrounds from IIT Roorkee and professional experience in analytics.
This course content overview highlights the initial curriculum steps, specifically focusing on setting up the R environment and providing a crash course for beginners.
This course features no prerequisites for beginners and includes practical exercises to reinforce learning through hands-on application.
This slide identifies the ideal student profile for the course as a Business Analyst, featuring an illustration of a professional presenting data on a flip chart.
One signal from 2020 suggests a need for more depth regarding the underlying logic of SVM and its specific use cases; note that the displayed update date does not prove this was addressed.
One signal from 2021 indicates a lack of explicit coverage of feature selection techniques; note that the displayed update date does not prove this was addressed.
The curriculum aligns with the stated goals of R users and data science beginners through its focus on setup and implementation.