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

Math    |    Beginner
  • 8 videos | 59m 21s
  • Includes Assessment
  • Earns a Badge
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.


  • 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


  • 2m 47s
  • 6m 8s
  • Locked
    3.  SVMs, Data Classification, and Hyperplanes
    11m 27s
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    4.  SVMs, Scaling, and Soft and Hard Margins
    6m 40s
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    5.  Working with Non-linear Data
    4m 58s
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    6.  The Optimization Problem for SVMs
    12m 32s
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    7.  Optimizing a Soft-margin Classifier
    12m 40s
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    8.  Course Summary
    2m 9s


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