SVM for Beginners: Support Vector Machines in R Studio

SVM for Beginners: Support Vector Machines in R Studio, available at $69.99, has an average rating of 4.35, with 57 lectures, 5 quizzes, based on 145 reviews, and has 56313 subscribers.

You will learn about Get a solid understanding of Support Vector Machines (SVM) Understand the business scenarios where Support Vector Machines (SVM) is 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 This course is ideal for individuals who are People pursuing a career in data science or Working Professionals beginning their Data journey or Statisticians needing more practical experience or Anyone curious to master SVM technique from Beginner to Advanced in short span of time It is particularly useful for People pursuing a career in data science or Working Professionals beginning their Data journey or Statisticians needing more practical experience or Anyone curious to master SVM technique from Beginner to Advanced in short span of time.

Enroll now: SVM for Beginners: Support Vector Machines in R Studio

Summary

Title: SVM for Beginners: Support Vector Machines in R Studio

Price: $69.99

Average Rating: 4.35

Number of Lectures: 57

Number of Quizzes: 5

Number of Published Lectures: 51

Number of Published Quizzes: 5

Number of Curriculum Items: 62

Number of Published Curriculum Objects: 56

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Get a solid understanding of Support Vector Machines (SVM)
Understand the business scenarios where Support Vector Machines (SVM) is 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

Who Should Attend

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

Target Audiences

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

You’re looking for a complete Support Vector Machines course that teaches you everything you need to create a 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 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 SVM.

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.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.

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

Cheers

Start-Tech Academy

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Setting up R Studio and R Crash Course

Lecture 1: Installing R and R studio

Lecture 2: Course Resources

Lecture 3: Basics of R and R studio

Lecture 4: This is a milestone!

Lecture 5: Packages in R

Lecture 6: Inputting data part 1: Inbuilt datasets of R

Lecture 7: Inputting data part 2: Manual data entry

Lecture 8: Inputting data part 3: Importing from CSV or Text files

Lecture 9: Creating Barplots in R

Lecture 10: Creating Histograms in R

Chapter 3: Machine Learning Basics

Lecture 1: Introduction to Machine Learning

Lecture 2: Building a Machine Learning Model

Chapter 4: Maximum Margin Classifier

Lecture 1: Course flow

Lecture 2: The Concept of a Hyperplane

Lecture 3: Maximum Margin Classifier

Lecture 4: Limitations of Maximum Margin Classifier

Chapter 5: Support Vector Classifier

Lecture 1: Support Vector classifiers

Lecture 2: Limitations of Support Vector Classifiers

Chapter 6: Support Vector Machines

Lecture 1: Kernel Based Support Vector Machines

Chapter 7: Creating Support Vector Machine Model in R

Lecture 1: The Data set for the Classification problem

Lecture 2: Importing Data into R

Lecture 3: Test-Train Split

Lecture 4: More about test-train split

Lecture 5: Classification SVM model using Linear Kernel

Lecture 6: Hyperparameter Tuning for Linear Kernel

Lecture 7: Polynomial Kernel with Hyperparameter Tuning

Lecture 8: Radial Kernel with Hyperparameter Tuning

Lecture 9: The Data set for the Regression problem

Lecture 10: SVM based Regression Model in R

Chapter 8: Appendix 1: Preprocessing and Preparing Data before making any model

Lecture 1: Gathering Business Knowledge

Lecture 2: Data Exploration

Lecture 3: The Data and the Data Dictionary

Lecture 4: Importing the dataset into R

Lecture 5: Univariate Analysis and EDD

Lecture 6: EDD in R

Lecture 7: Outlier Treatment

Lecture 8: Outlier Treatment in R

Lecture 9: Missing Value imputation

Lecture 10: Missing Value imputation in R

Lecture 11: Seasonality in Data

Lecture 12: Bi-variate Analysis and Variable Transformation

Lecture 13: Variable transformation in R

Lecture 14: Non Usable Variables

Lecture 15: Dummy variable creation: Handling qualitative data

Lecture 16: Dummy variable creation in R

Lecture 17: Correlation Matrix and cause-effect relationship

Lecture 18: Correlation Matrix in R

Chapter 9: Bonus Section

Lecture 1: The final milestone!

Lecture 2: About your certificate

Lecture 3: Bonus Lecture

Instructors

Start-Tech Academy
5,000,000+ Enrollments | 4.5 Rated | 160+ Countries

Rating Distribution

1 stars: 4 votes
2 stars: 2 votes
3 stars: 16 votes
4 stars: 46 votes
5 stars: 77 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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