Learning: How Support Vector Machines (SVM) algorithm works - Basic Intuition (Part -1) | NerdML
In this video, we explore Support Vector Machines (SVM) algorithm in some mathematical detail. It works to classify a linearly separable binary data set. We use Lagrange multipliers to maximize the width of the street given certain constraints. If needed, we transform vectors into another space, using a kernel function.
This video will help you to understand basic intuition of What is Support Vector Machine, hyperplane, Margin, Logistic Regression vs SVM, Support Vectors, Linearly Separability & Non-linearly Separability. I have divided Support Vector tutorial into several parts which will cover basic intuition, mathematics used behind finding algorithm's parameters.
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Please find the previous Video link - How K Nearest Neighbors Classifier Algorithm works | KNN Algorithm Using Python | NerdML : • How K Nearest Neighbors Classifier Algorit...
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Prerequisites
Basic understanding of Linear Algebra, Probability, Calculus, Matrix & Python programming including pandas, numpy, scikit learn & some visualization tools.
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