Machine Learning and AI: Support Vector Machines in Python

Support Vector Machines (SVM) are one of the most powerful machine learning models around, and this topic has been one that students have requested ever since I started making courses.These days, everyone seems to be talking about deep learning, but in fact there was a time when support vector machines were seen as superior to neural networks. One of the things you’ll learn about in this course is that a support vector machine actually is a neural network, and they essentially look identical if you were to draw a diagram.The toughest obstacle to overcome when you’re learning about support vector machines is that they are very theoretical. This theory very easily scares a lot of people away, and it might feel like learning about support vector machines is beyond your ability. Not so!In this course, we take a very methodical, step-by-step approach to build up all the theory you need to understand how the SVM really works. We are going to use Logistic Regression as our starting point, which is one of the very first things you learn about as a student of machine learning. So if you want to understand this course, just have a good intuition about Logistic Regression, and by extension have a good understanding of the geometry of lines, planes, and hyperplanes.This course will cover the critical theory behind SVMs:Linear SVM derivationHinge loss (and its relation to the Cross-Entropy loss)Quadratic programming (and Linear programming review)Slack variablesLagrangian DualityKernel SVM (nonlinear SVM)Polynomial Kernels, Gaussian Kernels, Sigmoid Kernels, and String KernelsLearn how to achieve an infinite-dimensional feature expansionProjected Gradient DescentSMO (Sequential Minimal Optimization)RBF Networks (Radial Ba