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.
Support Vector Machines - A Friendly Introduction
Overview
Syllabus
Introduction
Classification goal: split data
Perceptron algorithm
Split data - separate lines
How to separate lines?
Expanding rate
Perceptron Error
SVM Classification Error
Margin Error
Challenge - Gradient Descent
Which line is better?
The C parameter
Series of 3 videos
Thank you!
Taught by
Serrano.Academy
