Welcome to our repository where we delve into the implementation of Support Vector Machines (SVM) for educational purposes in our Machine Learning course. This repository is designed to facilitate a deep understanding of SVM through practical examples and custom implementations.
In this repository, you will find two main types of SVM implementations:
- SVM with scikit-learn: Utilize the powerful scikit-learn library to implement standard SVM models quickly and efficiently.
- SVM from Scratch: Challenge yourself by building SVM models from the ground up, gaining a deeper understanding of the underlying mechanics. You will also become familiar with the CVXOPT library.
We cover a range of scenarios and advanced topics in SVM, including:
- Kernel SVMs: Explore the use of different kernels such as linear, polynomial, and radial basis function (RBF) to understand how they influence the decision boundaries of the SVM.
- Multiclass Classification: Learn how to extend the binary classification capability of SVM to handle multiple classes, which is essential for dealing with more complex datasets.