Support vector machines,
in this course we will learn about Support Vector Machines (SVM), a powerful and widely-used machine learning algorithm for classification and regression tasks. The course begins with the foundational concepts of SVM, including the idea of maximizing the margin between data points and decision boundaries. You will explore the importance of support vectors and how they influence the optimal hyperplane. The course then introduces kernel methods, such as polynomial and radial basis function (RBF) kernels, which enable SVMs to handle non-linear data. You will learn how to apply SVMs in real-world scenarios using Python and scikit-learn, including data preprocessing, model training, evaluation, and optimization techniques like grid search and cross-validation. By the end of the course, you’ll be able to build robust SVM models, interpret their outputs, and apply them to tasks such as text classification, image recognition, and more. Data School
