Fundamentals of Machine Learning - Support Vector Machine (SVM) - Labs

This video tutorial covers support vector machines (SVMs), explaining their use in classification and regression tasks. It begins with an introduction to SVMs, followed by setting up necessary libraries like NumPy, SciPy, and Matplotlib. The tutorial then demonstrates data generation and visualization using Scikit-learn's make_blobs function. It explains linear SVMs, focusing on maximizing margins, and shows how to implement SVMs using Scikit-learn's SVC function. Finally, it discusses non-linear SVMs and the kernel trick, using the RBF kernel to handle complex decision boundaries.

How can visualization help in understanding the performance of an SVM?

Discuss the role of support vectors in the SVM model.

What challenges arise when trying to separate non-linearly separable data with SVM?

What is the kernel trick and how is it applied in SVM?