Support Vector Machine Classification in Python

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在此指导项目中,您将:

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
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

您要培养的技能

Machine LearningPython ProgrammingSupport Vector Machine (SVM)classificationSupervised Learning

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  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

指导项目工作原理

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在分屏视频中,您的授课教师会为您提供分步指导

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