Support Vector Machine Classification in Python

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In this Guided Project, you will:

import the dataset and perform training/testing set splits

Apply feature scaling for normalization

Build an SVM classifier and make Predictions

Build a Confusion Matrix and Visualize the results

2 hours
Intermediate
No download needed
Split-screen video
English
Desktop only

In this 1-hour long guided project-based course, you will learn how to use Python to implement a Support Vector Machine algorithm for classification. This type of algorithm classifies output data and makes predictions. The output of this model is a set of visualized scattered plots separated with a straight line. You will learn the fundamental theory and practical illustrations behind Support Vector Machines and learn to fit, examine, and utilize supervised Classification models using SVM to classify data, using Python. We will walk you step-by-step into Machine Learning supervised problems. With every task in this project, you will expand your knowledge, develop new skills, and broaden your experience in Machine Learning. Particularly, you will build a Support Vector Machine algorithm, and by the end of this project, you will be able to build your own SVM classification model with amazing visualization. In order to be successful in this project, you should just know the basics of Python and classification algorithms.

Skills you will develop

  • Machine Learning

  • Python Programming

  • Support Vector Machine (SVM)

  • classification

  • Supervised Learning

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Understand the concept of building a Support Vector Machine classification algorithm with a real-world example

  2. Import and explore the dataset and libraries: numpy, pandas and matplotlib

  3. Split the dataset into training set and testing set

  4. Apply feature scaling to normalize the input features

  5. Fit the SVM classifier to the dataset and making predictions

  6. Visualize training and testing sets results

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

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Frequently Asked Questions

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At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.

Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.