IT Professional Curricula     Enterprise Database Systems Solution Area     Math     Math Behind Support Vector Machines

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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, you'll know how to work with custom SVM classifiers and pre-built SVM classification and regression models.

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Objectives

Support Vector Machine (SVM) Math: Building & Applying SVM Models in Python discover the key concepts covered in this course use scikit-learn to generate blob data that is linearly separable separate a dataset into training and test sets code the steps to apply gradient descent to find the optimum hyperplane load a dataset from a CSV file into a pandas DataFrame and analyze it in preparation for binary classification generate a heatmap to visualize the correlations between features in a dataset build and evaluate an SVM classifier and recognize the importance of scaling the inputs to such a model use boxplots, a pair plot, and a heatmap to analyze a dataset in preparation for training a regression model build and evaluate an SVM regressor from the LIBSVM library summarize the key concepts covered in this course