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Support Vector Machines In R Programming

person icon Abhishek And Pukhraj

4.3

Support Vector Machines In R Programming

Learn Support Vector Machines in R Studio. Basic SVM models to kernel-based advanced SVM models of Machine Learning

updated on icon Updated on Nov, 2024

language icon Language - English

person icon Abhishek And Pukhraj

English [CC]

category icon Development ,Data Science,Machine Learning

Lectures -28

Resources -1

Duration -3 hours

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4.3

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Course Description

You're looking for a complete Support Vector Machines course that teaches you everything you need to create an SVM model in R, right?

You've found the right Support Vector Machines Techniques course!

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this advanced machine learning course.

If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real-world problems of business, this course will give you a solid base for that by teaching you some of the advanced techniques of machine learning, which are Support Vector Machines.

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem through SVM.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running the analysis is even more important i.e. before running the analysis it is very important that you have the right data and do some pre-processing on it. After running the analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in a Global Analytics Consulting firm, we have helped businesses solve their business problems using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course.

Our Promise:

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Who this course is for?

  • People pursuing a career in data science.
  • Working Professionals beginning their Data journey.
  • Statisticians need more practical experience.
  • Anyone curious to master SVM technique from Beginner to Advanced in a short span of time.

Goals

  • Get a solid understanding of Support Vector Machines (SVM).
  • Understand the business scenarios where Support Vector Machines (SVM) are applicable.
  • Tune a machine learning model's hyperparameters and evaluate its performance.
  • Use Support Vector Machines (SVM) to make predictions.
  • Implementation of SVM models in R programming language - R Studio.

Prerequisites

  • Students will need to install R and R Studio software but we have a separate lecture to help you install the same.
Support Vector Machines In R Programming

Curriculum

Check out the detailed breakdown of what’s inside the course

Setting up R Studio
9 Lectures
  • play icon Installing R and R studio 05:52 05:52
  • play icon Course Resources
  • play icon Basics of R and R studio 10:47 10:47
  • play icon Packages in R 10:52 10:52
  • play icon Inputting data part 1: Inbuilt datasets of R 04:21 04:21
  • play icon Inputting data part 2: Manual data entry 03:11 03:11
  • play icon Inputting data part 3: Importing from CSV or Text files 06:49 06:49
  • play icon Creating Barplots in R 13:42 13:42
  • play icon Creating Histograms in R 06:01 06:01
Machine Learning Basics
2 Lectures
Tutorialspoint
Maximum Margin Classifier
4 Lectures
Tutorialspoint
Support Vector Classifier
2 Lectures
Tutorialspoint
Support Vector Machines
1 Lectures
Tutorialspoint
Creating Support Vector Machine Model in R
9 Lectures
Tutorialspoint

Instructor Details

Abhishek and Pukhraj

Abhishek and Pukhraj

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