Welcome to the Support Vector Machine
In this session, we’ll dive into how SVM works and how to build one using Python. You’ll learn:
What is an SVM and how it finds the best decision boundary
Concepts of hyperplanes, support vectors, and margins
Difference between linear and non-linear classification
How to use kernel tricks (like RBF) to handle complex data
How to train and test an SVM model using Scikit-learn
