Introduction to Support Vector Machines

This course is designed  to help the learners start from the very foundation of the SVM algorithm and quickly become a professional in implementing the algorithm on business cases.

  • icon-videos 2 Videos
  • icon-data 6 Data Cases
  • icon-reading 2 Readings
  • icon-quiz 4 Quizzes
Support Vector Machines in ML
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    Difficulty: Intermediate

    Foundational knowledge or experience in statistics or analytics is recommended.

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    Duration: Approximately 8 hours

Course Overview

  • Support Vector Machines (SVM) are used in a variety of applications such as face detection, classification of emails, news articles, and web pages, and handwriting recognition.
  • Develop a working understanding of SVM techniques and Kernel tricks for both Regression and Classification problems.
  • Learn all essentials of the algorithm through an experiential learning approach.
  • Gain practical experience by applying the understanding in 4 real-world business case scenarios.

What’s included

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Lifetime Access

Access this project for life once completed

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Flexible Scheduling

Start learning online immediately, at your own pace

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Desktop Only

We recommend completing this project on a desktop

Syllabus

This module introduces you to the fundamental concepts of Support Vector Machines (SVM) for the classification tasks.

This module introduces you to the fundamental concepts of SVM for the regression tasks.

How it Works

Learn new skills that will boost your career by enrolling in courses across data analytics, data science, ML and AI. These courses will utilize readings, videos, quizzes, data cases, and even coding exercises to teach you skills and concepts in a way that will solidify your new knowledge for hands-on application.

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