Support Vector Machine Classification in Python

4.4
stars
127 ratings
Offered By
Coursera Project Network
5,420 already enrolled
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

Clock2 hours
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop 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 LearningPython ProgrammingSupport Vector Machine (SVM)classificationSupervised 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

Reviews

TOP REVIEWS FROM SUPPORT VECTOR MACHINE CLASSIFICATION IN PYTHON

View all reviews

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.