Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore support vector machines (SVMs) in this beginner-friendly 31-minute video that requires minimal mathematical background. Learn about classification goals, the perceptron algorithm, and data separation techniques. Discover concepts like expanding rate, perceptron error, SVM classification error, and margin error. Tackle a gradient descent challenge and understand the importance of the C parameter in SVMs. Gain insights into choosing the best separating line for classification tasks. This video is part of a three-part series on machine learning algorithms, complementing previous videos on linear and logistic regression.