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  • Reading time:11 mins read

What you will learn

Use R for Data Science and Machine Learning

Provides the entire toolbox you need to become a NLP engineer

Learn how to pre-process data

Apply your skills to real-life business cases

Able to perform web scraping

Learn text mining

able to perform sentimental analysis on any text

Description

Caution before taking this course:

This course does not make you expert in R programming rather it will teach you concepts which will be more than enough to be used in machine learning and natural language processing models.

About the course:


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In this practical, hands-on course you’ll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

Our main objective is to give you the education not just to understand the ins and outs of the R programming language, but also to learn exactly how to become a professional Data Scientist with R and land your first job.

This course covers following topics:

1. R programming concepts: variables, data structures: vector, matrix, list, data frames/ loops/ functions/ dplyr package/ apply() functions

2. Web scraping: How to scrape titles, link and store to the data structures

3. NLP technologies: Bag of Word model, Term Frequency model, Inverse Document Frequency model

4. Sentimental Analysis: Bing and NRC lexicon

5. Text mining

By the end of the course you’ll be in a journey to become Data Scientist with R and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.

English
language

Content

Introduction
Introduction
No background required!!
What will you learn?
What is R?
Essentials: R programming
Interface of R-studio
Theory: Installing packages in R
Installing packages in r
Data types in R
Assignment operator in R
Create multiple variables in R
Concatenate variables in R
Variables in R
Rule for naming a variable
Data Types and Type-casting
IMPORTANT: Data Structures in R
Assignment operator in R
Theory: Vectors in R
Access vector items
Generating sequenced vector
Vectors in R
Theory: List in R
Check if item exists in list
Add item to the list
List in R
Matrices in R
Relational data
Data Frames in R
Theory: Access items from data frame
Add rows to the data frame
Add columns to the data frame
Data Frame in R
Use data frame
Factor in R
Miscellaneous
Math in R
Miscellaneous operators in R
table function in R
Building Logic in R
Loops in R
Theory: Concepts of loops
while loop in R
for loop in R
The apply function in R
Theory: Function in R
Functions in R
Default argument in R
The “dplyr” package to handle data
Theory: Introduction to the dplyr package
Select function in R
Select function in R
Filter function in R
Filter function in R
Theory: Mutate and Transmute function in R
Mutate and Transmute function in R
The diff() function in R
Theory: Pipe operator in R
Pipe operator in R (Do not miss this video)
Introduction to Text mining
Text Mining in R
Common Techniques
Tokenization in R
Stemming in R
Natural Language Processing
Text Mining Applications
Important Terminologies
Important Terms in Text Mining
What is web scraping?
Project: Sentimental Analysis with R
Tools for webscraping in R
Installing rvest package in R
Read html contents
Use locator to get html nodes
Using dplyr
Data Manipulation
Change column name
Get all links
Cleaning the data
Clean data continued..
Filter the data
Get content using scraping
Split the data
Use loops for repeated tasks
Creating data frame
Refine the data from data frame
Count rows and columns
Theory: What is corpus?
Theory: Term Document Matrix
Theory: Bag of Word Models
Theory: Vector Space Model
Term Frequency — IMPORTANT
Inverse Document Frequency model
Corpus and Term Document Matrix
Remove Sparse terms
Frequency distributions
Theory: Wordclouds in R
Wordcloud
Clean the corpus
Remove stop words
Season 2 of Big Bang Theory
Frequency distributions
Plot a bar graph
Add theme to bar graph
What is sentimental analysis?
How sentimental analysis work?
Bing and NRC lexicon
How sentiments classification is done?
Tokenization
Theory: Reshape in R
Melting in R
Casting in R
Using bing lexicon
Using NRC lexicon
Plot ribbon plots