Support Vector Machine (SVM) Math: Building & Applying SVM Models in Python

Support vector machines (SVMs) are a popular tool for machine learning enthusiasts at any level. They offer speed and accuracy are computationally uncomplicated and work well with small datasets. In this course learn how to implement a soft-margin SVM classifier using gradient descent in the Python programming language and the LIBSVM library to build a support vector classifier and regressor. For your first task generate synthetic data that can be linearly separated by an SVM binary classifier implement the classifier by applying gradient descent and train and evaluate the model. Moving on learn how to use a pre-built SVM classifier supplied by the LIBSVM module. Then use LIBSVM to train a support vector regressor evaluate it and use it for predictions. Upon completion youll know how to work with custom SVM classifiers and pre-built SVM classification and regression models.