# Support Vector Machine (SVM) Math: A Conceptual Look at Support Vector Machines

Math    |    Beginner
• 8 videos | 59m 21s
• Includes Assessment
Rating 4.0 of 4 users (4)
Simple to use yet efficient and reliable, support vector machines (SVMs) are supervised learning methods popularly used for classification tasks. This course uncovers the math behind SVMs, focusing on how an optimum SVM hyperplane for classification is computed. Explore the representation of data in a feature space, finding a hyperplane to separate the data linearly. Then, learn how to separate non-linear data. Investigate the optimization problem for SVM classifiers, looking at how the weights of the model can be adjusted during training to get the best hyperplane separating the data points. Furthermore, apply gradient descent to solve the optimization problem for SVMs. When you're done, you'll have the foundational knowledge you need to start building and applying SVMs for machine learning.

## WHAT YOU WILL LEARN

• Discover the key concepts covered in this course
Recognize the place of support vector machines (svms) in the machine learning landscape
Outline how svms can be used to classify data, how hyperplanes are defined, and the qualities of an optimum hyperplane
Recall the qualities of an optimum hyperplane, outline how scaling works with svm, distinguish soft and hard margins, and recognize when and how to use either margin
• Recall the techniques that can be applied to classify data that are not linearly separable
Formulate the optimization problem for support vector machines
Apply the gradient descent algorithm to solve for the optimum hyperplane
Summarize the key concepts covered in this course

## IN THIS COURSE

• 3.  SVMs, Data Classification, and Hyperplanes
• 4.  SVMs, Scaling, and Soft and Hard Margins
• 5.  Working with Non-linear Data
• 6.  The Optimization Problem for SVMs
• 7.  Optimizing a Soft-margin Classifier
• 8.  Course Summary

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