Support Vector Machine Tutorial Using R | SVM Algorithm Explained | Data Science Training | Edureka

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This session is dedicated to how SVM works, the various features of SVM and how it used in the real world. The following topics will be covered today:

(01:15) Introduction to machine learning
((04:15) What is Support Vector Machine (SVM)?
(06:19) How does SVM work?
(09:35) Non-linear SVM
(11:20) SVM Use case
(12:43) Hands-On

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#svmalgorithm #svmwithr #svmclassifier #datascience #datasciencetutorial #datasciencewithr #datasciencecourse #datascienceforbeginners #datasciencetraining #datasciencetutorial

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About the Course

Edureka's Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
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Why Learn Data Science?

Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.

After the completion of the Data Science course, you should be able to:

1. Gain insight into the 'Roles' played by a Data Scientist

2. Analyze Big Data using R, Hadoop and Machine Learning

3. Understand the Data Analysis Life Cycle

4. Work with different data formats like XML, CSV and SAS, SPSS, etc.

5. Learn tools and techniques for data transformation

6. Understand Data Mining techniques and their implementation

7. Analyze data using machine learning algorithms in R

8. Work with Hadoop Mappers and Reducers to analyze data

9. Implement various Machine Learning Algorithms in Apache Mahout

10. Gain insight into data visualization and optimization techniques

11. Explore the parallel processing feature in R

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Who should go for this course?

The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:

1. Developers aspiring to be a 'Data Scientist'

2. Analytics Managers who are leading a team of analysts

3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics

4. Business Analysts who want to understand Machine Learning (ML) Techniques

5. Information Architects who want to gain expertise in Predictive Analytics

6. 'R' professionals who want to captivate and analyze Big Data

7. Hadoop Professionals who want to learn R and ML techniques

8. Analysts wanting to understand Data Science methodologies.

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Комментарии

This simple way of putting SVM, has made my life much easier much thanks to you. God Bless!

zuber

This tutorial is pretty awesome !! Thanks for the video, it has helped me a lot!

juancorderoromero

thanks maam. it helped me to clear my doubts 😊

meghnam

Thanks a lot mam.. You have done a great job

tapanjeetroy

Thank you so much. it is very helpful !

md.salauddinkhan

best explanation. cleared all doubts
.Thank you very much.

rsivaranganayakulu

Thanks a lot! This was really helpful!

MrNunatack

thanks for sharing this ...its is very informative.

WasimShaikh-lvne

Want to ask, how can we fill the N column? As we know that we want to predict that part. How can we get the data in the N column before?

ainunhasanah

Plz make tutorial/guider on how to use SVM algorithm for raster layer(geospatial modeling)

BikramSingh-qbuh

That is awesome. Thank you very much for sharing

yessohedjoukou

This is amazing! where can I get the datasets? Thanks

poojarawat

very nice teaching way along with programming. thank you so much

ravikhandsiya

how can i graph the svm or what plots can i make?

oscartrilleras

Thank you. Can you please tell me what programming language we are using in Rstudio?

ankitalohar

This is amazing! where can I get the datasets? Thanks

muhamadmaqsudhossain

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