Topics Course
Published on April 16, 2017 by Edureka

This Edureka Linear Regression tutorial will help you understand all the basics of linear regression machine learning algorithm along with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:

1) Introduction to Machine Learning
2) What is Regression?
3) Types of Regression
4) Linear Regression Examples
5) Linear Regression Use Cases
6) Demo in R: Real Estate Use Case

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#LinearRegression #Datasciencetutorial #Datasciencecourse #datascience

How it Works?

1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get Lifetime Access to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!

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About the Course

Edureka’s Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling 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. Analyse 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. Analyse 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

Please write back to us at sales@edureka.co or call us at +918880862004 or 18002759730 for more information.

Website: www.edureka.co/data-science
Facebook: www.facebook.com/edurekaIN/
Twitter: twitter.com/edurekain
LinkedIn: www.linkedin.com/company/edureka

Customer Reviews:

Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, “Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now…Thanks EDUREKA and all the best. “

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84 Comments on "Linear Regression Algorithm | Linear Regression in R | Data Science Training | Edureka"

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surya narayana
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surya narayana
3 days 23 hours ago

Well Explained sir . Its really help us a lot but there is one suggestion from my side that it would be even better that u could explain the summary results . I mean say there are various parameters in the summary so not everyone would be familiar with everything so it would be even better if that explanation of parameters was done . Overall the tutorial was really good sir. Thanks a lot for your effort:)

edureka!
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edureka!
3 days 5 hours ago

Hey Surya, thank you for appreciating our work. We will definitely look into your suggestions. Do subscribe and stay connected with us. Cheers 🙂

vijila p
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vijila p
14 days 16 hours ago

please upload a session for implementing weighted association rule mining in R

edureka!
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edureka!
13 days 22 hours ago

Hey Vijila, sure. We will definitely look into your suggestions. Subscribe and stay tuned, we update our channel regularly. Cheers 🙂

kollu nimshi
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kollu nimshi
16 days 14 hours ago

Please forward data sets

edureka!
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edureka!
13 days 2 hours ago

Hey Kollu, mention your email address and we will send it over. Cheers 🙂

Ash Dhuri
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Ash Dhuri
1 month 9 days ago

Plz forward d dataset

edureka!
Guest
edureka!
1 month 7 days ago

Hey Ash, we have forwarded all the data-sets to you. Let us know if you need anything else. Cheers 🙂

mo kh
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mo kh
2 months 17 hours ago

there is a fault in the minute 47:03, it should be : " predic <- predict(model, testing_data) " not " predic <- predict(model, test) "thank you for this great video

edureka!
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edureka!
2 months 4 hours ago

Hey Mo, thank you for sharing this. We will definitely look into this and try to rectify the errors. Cheers 🙂

Anuradha Agarwal
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Anuradha Agarwal
2 months 8 days ago

Can you please share the dataset and R code for further reference

edureka!
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edureka!
2 months 8 days ago

Hey Anuradha, sure. Mention your email address and we will get in touch with you. Cheers 🙂

Gary Parmar
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Gary Parmar
2 months 16 days ago

Nice presentation

Pallavi Nagrale
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Pallavi Nagrale
2 months 26 days ago

Awesome.. to the point, clear!

edureka!
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edureka!
2 months 22 days ago

Thank you for watching our video. Do subscribe, like and share to stay connected with us. Cheers 🙂

Upendra Roul
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Upendra Roul
3 months 21 days ago

Can I get the data set of the examples used in R ?

edureka!
Guest
edureka!
3 months 19 days ago

Hey Upendra, sure. Mention your email address and we will mail it over. Cheers 🙂

Rahul Arora
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Rahul Arora
4 months 14 days ago

very nice lectures,really helpful

edureka!
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edureka!
4 months 13 days ago

Thank you for watching our video. Do subscribe, like and share to stay connected with us. Cheers 🙂

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