🎓 Support Vector Machines (SVM) Implementations

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.

📄 Overview

In this repository, you will find two main types of SVM implementations:

1. SVM with scikit-learn: Utilize the powerful scikit-learn library to implement standard SVM models quickly and efficiently.
2. 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.

🌟 Features

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.

📚 Contents

The repository is organized as follows:

• README.md: This file providing an overview of the repository.
• SVM_fully from scratch (Helper).pdf: A detailed PDF guide to understanding and implementing SVM from scratch.
• SVM_implementation.ipynb: Jupyter notebook demonstrating the implementation of SVM using scikit-learn and from scratch.
• evaluate.py: A Python script to evaluate the performance of the SVM models.

🚀 Getting Started

To get started with the code in this repository, follow these steps:

1. Clone the repository:

   git clone https://github.com/shining0611armor/svm-implementations.git
   cd svm-implementations

2. Explore the Jupyter notebooks:

   jupyter notebook

   Open and run the SVM_implementation.ipynb notebook to see the SVM implementations in action.

📈 Usage

• SVM with scikit-learn:

  • Quickly implement SVM models with minimal code.
  • Experiment with different kernels and hyperparameters.

• SVM from Scratch:

  • Gain a deep understanding of the mathematical foundations of SVM.
  • Learn how to solve the quadratic optimization problem using CVXOPT.

🎯 Goals

By the end of this course, you will:

• Have a strong understanding of SVM and its applications in machine learning.
• Be able to implement SVM models using scikit-learn for quick and efficient solutions.
• Gain the ability to build SVM models from scratch, enhancing your problem-solving and coding skills.

👩‍🏫 About the Instructor

I am Mehran Tamjidi, a passionate educator and researcher in the field of machine learning and artificial intelligence. This repository is a part of my efforts to provide comprehensive and practical knowledge to students and enthusiasts.

📫 Contact

Feel free to reach out if you have any questions or suggestions:

• Email: mehrant.0611@gmail.com
• GitHub: shining0611armor

Happy Learning! 😊