Support Vector Machines

Support Vector Machines (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It works by finding the optimal hyperplane that best separates data points into different classes in a high-dimensional space, maximizing the margin between the classes. SVMs are particularly effective in high-dimensional spaces and are versatile through the use of kernel functions to handle non-linear decision boundaries.

Also known as: SVM, Support Vector Classifier, SVC, Support Vector Regression, SVR

Why learn Support Vector Machines?

Developers should learn SVMs when working on classification problems with clear margins of separation, such as text categorization, image recognition, or bioinformatics, where data is not linearly separable. They are useful for small to medium-sized datasets and when interpretability of the model is less critical compared to performance, as SVMs can achieve high accuracy with appropriate kernel selection. However, they may be less efficient for very large datasets due to computational complexity.

Learning Resources

• docs: Scikit-learn SVM Documentation
• video: Support Vector Machines Explained - StatQuest
• course: Coursera: Machine Learning by Andrew Ng (SVM Section)
• book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Book)