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

Math    |    Intermediate
  • 10 videos | 1h 34m 21s
  • Includes Assessment
  • Earns a Badge
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.


  • 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


  • 2m 48s
  • 10m 59s
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    3.  Preparing Data for an SVM Classifier
    9m 2s
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    4.  Training and Evaluating an SVM Model
    11m 43s
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    5.  Analyzing a Dataset for a Binary Classifier
    11m 52s
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    6.  Visualizing the Relationships between Features
    7m 52s
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    7.  Training and Evaluating the LIBSVM Classifier
    12m 48s
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    8.  Analyzing the Data for Support Vector Regression
    12m 8s
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    9.  Building a Support Vector Regressor
    12m 50s
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    10.  Course Summary
    2m 19s


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