Support Vector Machines (SVMs) - Explained

This video explains Support Vector Machines (SVM) from first principles, covering the classification problem, decision boundaries, maximum margin intuition, support vectors, margin maximization, and the mathematical formulation. It also introduces the kernel trick for non-linearly separable data. A clear and visual explanation of SVM geometry, optimization, and machine learning fundamentals for anyone studying AI, data science, or pattern recognition. *Related Videos* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The Hessian Matrix: https://youtu.be/9tp1kULwU2w The Jacobian Matrix: https://youtu.be/6FesMicc844 B…