This lecture introduces the concept of using Support Vector Machines for multi-class classification, explaining how to train separate classifiers for each class to better distinguish between them. It covers the process of constructing key binary classifiers and how the decision-making process works when classifying data points. The lecture also provides examples of multi-class SVM solutions for different scenarios, highlighting the importance of support vectors in tightening the classification boundaries.