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Course: Machine Learning Adv: Support Vector Machines (SVM) in R

Machine Learning Adv: Support Vector Machines (SVM) in R

  • Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App
  • Self-Paced
About this Course

You're looking for a complete Support Vector Machines course that teaches you everything you need to create a Support Vector Machines model in Python, 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 Machine learning advanced 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 technique 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 Decision tree.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running 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 Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

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.

Go ahead and click the enroll button, and I'll see you in lesson 1!

Cheers

Start-Tech Academy

Who this course is for:

  • People pursuing a career in data science
  • Working Professionals beginning their Data journey
  • Statisticians needing more practical experience
  • Anyone curious to master SVM technique from Beginner to Advanced in short span of time
Basic knowledge
  • Students will need to install R and R Studio software but we have a separate lecture to help you install the same
What you will learn
  • Get a solid understanding of Support Vector Machines
  • Understand the business scenarios where Support Vector Machines is applicable
  • Tune a machine learning model's hyperparameters and evaluate its performance.
  • Use Support Vector Machines to make predictions
Curriculum
Number of Lectures: 26
Total Duration: 03:04:22
Setting up R Studio and R Crash Course
  • Installing R and R studio  
  • Basics of R and R studio  
  • Packages in R  
  • Inputting data part 1: Inbuilt datasets of R  
  • Inputting data part 2: Manual data entry  
  • Inputting data part 3: Importing from CSV or Text files  
  • Creating Barplots in R  
  • Creating Histograms in R  
Machine Learning Basics
  • Introduction to Machine Learning  
  • Building a Machine Learning Model  
Maximum Margin Classifier
  • Course flow  
  • The Concept of a Hyperplane  
  • Maximum Margin Classifier  
  • Limitations of Maximum Margin Classifier  
Support Vector Classifier
  • Support Vector classifiers  
  • Limitations of Support Vector Classifiers  
Support Vector Machines
  • Kernel Based Support Vector Machines  
Creating Support Vector Machine Model in R
  • The Data set for the Classification problem  
  • Importing Data into R  
  • Test-Train Split  
  • Classification SVM model using Linear Kernel  
  • Hyperparameter Tuning for Linear Kernel  
  • Polynomial Kernel with Hyperparameter Tuning  
  • Radial Kernel with Hyperparameter Tuning  
  • The Data set for the Regression problem  
  • SVM based Regression Model in R  
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