Text Mining for Beginners - Using Case Study Approach!

Learn text mining and data mining in order to make your first move towards learning Natural Language Processing.

Created by : Rated :4.6 7548 views

Last Updated: 2022-07-20 08:07:49 English Course Type: Self Learning

Students will learn the text mining concepts and practical approach easily from scratch.
You would be able to build a word cloud and find out important insights from it using case study approach.
You can work as a freelancer on text mining and NLP projects and earn as a freelancer.
You can get a high paying job as an NLP Engineer in analytics firm.

Are you ready to discover interesting patterns, extract useful knowledge, and support decision making by mining and analyzing your Social Media Data i.e. Unstructured Data/ Text data?

Are you ready to build your 1st Word Cloud that gives you Visual insights on your Text Data within Few Minutes!?

Now if go by facts and figures than by 2020, there will be around 2.9 billion social network users around the globe. And a vast amount of new information and data is generated every day i.e. Over 2.5 quintillion bytes of data are created every single day, and it's only going to grow from there. By 2020, it's estimated that 1.7MB of data will be created every second for every person on earth. And 90% of world's data is in an unstructured format (text data), which makes mining this unstructured data i.e. text mining one of the most sought after skills in Data Science and Machine learning.

LEARN IT FROM EXPERIENCED DATA SCIENCE GUY!

Hello Everyone!......... My name is Bhaumik S. Shah and I am a data science guy with master's degree in it and I have a good amount of experience of working on different data science projects related to Machine learning (Regression, Classification, etc.), Time Series, Text Mining, Natural Language Processing, etc. as a freelancer. I have designed this course to make you familiar with text mining with a practical case study approach unlike other courses out there which focuses a lot on theory rather than practical approach. So in this course, we will start text mining from scratch, so it's okay if you are not familiar with this topic. We will cover some important/ required theory related to text mining in the beginning and we will straight move towards one case study using R for implementing it! . And you will also have access to future classes for FREE that will be updated in this course soon, in order to keep it relevant to the changing scenarios.

Should be aware of basic R programming
Basic Internet using skills.
Should have interest in analyzing and deriving insights from social media data i.e. text data
Basic knowledge of machine learning algorithms, Data mining and NLP will be beneficial but not necessary.

People who wants to learn Text Mining from scratch and it's important concepts.
People who wants to make their base solid in mining text data.
People who wants to learn how to analyze text data through case study approach.
People who wants to work on data science projects related to text mining , text analyics etc as a freelancer.
People who are planning to learn Natural Language Processing

Section 1 : Introduction to R Programming 2 Lectures 00:05:43
Lecture 1 :
Installing R and R Studio Preview
Lecture 2 :
R Studio - Interface
Section 2 : Reading Files in R 3 Lectures 00:13:58
Lecture 1 :
Reading CSV files in R
Lecture 2 :
Reading Text files in R
Lecture 3 :
Reading Excel files in R
Section 3 : Text Mining 4 Lectures 00:15:51
Lecture 1 :
Text Mining Package in R
Lecture 2 :
Analysis Using Term Document Matrix
Lecture 3 :
Quiz
Answer based on what you learned from above lectures
Lecture 4 :
Introduction - Text Mining
Section 4 : Case Study - Using Twitter Data in R! 3 Lectures 00:30:12
Lecture 1 :
Implementation using twitter data in R 4.1
In this section, you'll learn using a case study in R!
Lecture 2 :
Implementation using twitter data in R 4.2
Lecture 3 :
Implementation using twitter data in R 4.3
Section 5 : Word Cloud Creation 2 Lectures 00:08:24
Lecture 1 :
Building word cloud in R
Lecture 2 :
Quiz
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