Course: Support Vector Machine (SVM) Math training

$59.00
$71.39 incl. vat

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duration: 4 hours |

Language: English (US) |

access duration: 90 days |

Details

Simple to use yet efficient and reliable, support vector machines (SVMs) are supervised learning methods popularly used for classification tasks. The Support Vector Machine (SVM) algorithm allows you to make predictions by dividing data into groups.

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 and finding a hyperplane to separate the data linearly. You’ll also learn how to implement and custom 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.

Result

After completing this course, you'll have the foundational knowledge to start building and applying SVMs for machine learning. Furtermore, you'll know how to work with custom SVM classifiers and pre-built SVM classification and regression models.

Prerequisites

You have experience with machine learning and the Python programming language and are familiar with classification tasks.

Target audience

Software Developer, Web Developer

Content

Support Vector Machine (SVM) Math

4 hours

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

  • 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.

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

Course options

We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.

Optional practice exam (trial exam)

To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam. 

Optional practice lab

To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediatelyThe lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience. 

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