Text Mining In R | Natural Language Processing | Data Science Certification Training | Edureka

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** Data Science Certification using R: https://www.edureka.co/data-science-r-programming-certification-course **
In this video on Text Mining In R, we’ll be focusing on the various methodologies used in text mining in order to retrieve useful information from data. The following topics are covered in this session:

(01:18) Need for Text Mining
(03:56) What Is Text Mining?
(05:42) What is NLP?
(07:00) Applications of NLP
(08:33) Terminologies in NLP
(14:09) Demo

Blog Series: http://bit.ly/data-science-blogs

Data Science Training Playlist: http://bit.ly/data-science-playlist

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#textmining #textminingwithr #naturallanguageprocessing #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.

For online Data Science training, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

Comments

edureka! says:

Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Data Science Training Certification Curriculum, Visit our Website: http://bit.ly/37q65Oc

Gulzar Hussain says:

thank you so much edureka for bringing such video! I love it.

Thej Kiran says:

Amazing! Miss! You killed it! thank You very much!

piyush tripathi says:

Very nice 👍

Anm Faisal says:

Thank you for a great session. Best explained

Josephine Pasipanodya says:

Is text mining similar to thematic analysis

Karthik Ravinatha says:

i am getting this error
Error in Corpus(DirSource("F:/textmining")) :

could not find function "Corpus"
inspect(docs)

Error in inspect(docs) : could not find function "inspect"

i have installed packages nlp and tm
please resolve thank you

Priyanka Shukla says:

and how to analyze Reviews given in . CSV file?

Priyanka Shukla says:

what extension of file in TextMining folder?

lolao says:

Best explanation ever,, thank you!

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