04 -- Hands On ML -- SVM

All the references are taken from the book -- Hands On Machine Learning with Scikit-learn, Keras & Tensorflow by Aurelien Geron. Notebook for this article can be found here. Support Vector Machines can be used for linear or non-linear classification, regression and even outlier detection. It is well suited for complex-small or medium-sized datasets. SVMs are also sensitive to feature scaling, if the feature are standardized it will generalize better.