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Machine Learning and AI: Support Vector Machines in Python - Artificial Intelligence and Data Science Algorithms in Python for Classification and Regression
Sure! Here’s a brief article on Support Vector Machines (SVM) in Python.
Support Vector Machines (SVM) is a supervised machine learning algorithm that can be used for classification and regression problems1. SVMs are effective in high dimensional spaces and are still popular among many machine learning practitioners2.
To implement SVMs in Python, you can use the Scikit-learn library3. The Scikit-learn library provides an easy-to-use interface for implementing SVMs in Python3.
Here’s a simple example of how to implement SVMs using Scikit-learn:
What you'll learn
- Apply SVMs to practical applications: image recognition, spam detection, medical diagnosis, and regression analysis
- Understand the theory behind SVMs from scratch (basic geometry)
- Use Lagrangian Duality to derive the Kernel SVM
- Understand how Quadratic Programming is applied to SVM
- Support Vector Regression
- Polynomial Kernel, Gaussian Kernel, and Sigmoid Kernel
- Build your own RBF Network and other Neural Networks based on SVM
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