Support Vector Machines

Support Vector Machines (SVM) training focuses on a powerful supervised machine learning algorithm used for classification and regression tasks. This training explains how SVM works by finding the optimal hyperplane that separates different classes with maximum margin. You will learn how kernels help transform non-linear data into higher dimensions for better classification. The course covers concepts such as support vectors, margin optimization, and decision boundaries. It also explains how SVM is applied in real-world scenarios like image classification, text categorization, and pattern recognition. You will learn how to evaluate model performance and tune parameters for better accuracy. This training is ideal for learners who want to build strong foundations in machine learning algorithms.