Support Vector Machine , or SVM , is one of the most popular Supervised Learning algorithms used for Classification, Regression, and anomaly detection problems. Learn more on Scaler Topics. Support Vector Machines (SVMs) are a type of supervised machine learning algorithm used for classification and regression tasks. They are widely used in various fields, including pattern ... Learn how SVMs are used for classification and regression problems, and how they find a hyperplane that maximizes the margin between classes. See examples of SVM implementation in Python using sklearn library and kernel trick. Learn what SVM is, how it works, and why it is effective for classification and regression tasks. Explore its features, examples, applications, advantages, and disadvantages with Ze Learning Labb's courses.
