Support Vector Machines

When discussing classification, support vector machines are known to be a capable and efficient technique to learn and predict with high accuracy within a quick time frame. Yet, their black box means to do so make the practical users quite circumspect about relying on it, without much understanding of the how and why of its predictions.

Originally formulated for two-class classification problems, support vector machines (SVMs) are now accepted as powerful tools for developing pattern classification and function approximation systems. Recent developments in kernel-based methods include kernel classifiers and regressors and their variants, advancements in generalization theory, and various feature selection and extraction methods.

Learn to use Support Vector Machines for Regression from a Data Science expert. Code templates included.

Learn Support Vector Machines in Python. Covers basic SVM models to Kernel-based advanced SVM models of Machine Learning.

Learn Support Vector Machines in R Studio. Basic SVM models to kernel-based advanced SVM models of Machine Learning.

Learn Support Vector Machines in Python. Covers basic SVM models to Kernel-based advanced SVM models of Machine Learning.

Learn to apply Support Vector Machines for Classification from a Data Science expert. Code templates included.