Support Vector Machine Classification in Python


Resource | v1 | created by coursera-bot |
Type Course
Created unavailable
Identifier unavailable

Description

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.

Relations

Edit details Edit relations Attach new author Attach new topic Attach new resource
0.0 /10
useless alright awesome
from 0 reviews
Write comment Rate resource Tip: Rating is anonymous unless you also write a comment.
Resource level 0.0 /10
beginner intermediate advanced
Resource clarity 0.0 /10
hardly clear sometimes unclear perfectly clear
Reviewer's background 0.0 /10
none basics intermediate advanced expert
Comments 0
Currently, there aren't any comments.