Data Science: NLP and Sentimental Analysis in R

Data Science: NLP and Sentimental Analysis in R, available at $54.99, has an average rating of 4.85, with 106 lectures, based on 12 reviews, and has 5007 subscribers.

You will learn about 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 This course is ideal for individuals who are You should take this course if you want to become a Data Scientist or if you want to learn about the field or You should take this course if you want to learn text mining and text analysis doing fun projects or You should take this course if you want to learn web scraping It is particularly useful for You should take this course if you want to become a Data Scientist or if you want to learn about the field or You should take this course if you want to learn text mining and text analysis doing fun projects or You should take this course if you want to learn web scraping.

Enroll now: Data Science: NLP and Sentimental Analysis in R

Summary

Title: Data Science: NLP and Sentimental Analysis in R

Price: $54.99

Average Rating: 4.85

Number of Lectures: 106

Number of Published Lectures: 106

Number of Curriculum Items: 106

Number of Published Curriculum Objects: 106

Original Price: $24.99

Quality Status: approved

Status: Live

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

Who Should Attend

You should take this course if you want to become a Data Scientist or if you want to learn about the field
You should take this course if you want to learn text mining and text analysis doing fun projects
You should take this course if you want to learn web scraping

Target Audiences

You should take this course if you want to become a Data Scientist or if you want to learn about the field
You should take this course if you want to learn text mining and text analysis doing fun projects
You should take this course if you want to learn web scraping

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:

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: No background required!!

Lecture 3: What will you learn?

Lecture 4: What is R?

Chapter 2: Essentials: R programming

Lecture 1: Interface of R-studio

Lecture 2: Theory: Installing packages in R

Lecture 3: Installing packages in r

Lecture 4: Data types in R

Lecture 5: Assignment operator in R

Lecture 6: Create multiple variables in R

Lecture 7: Concatenate variables in R

Lecture 8: Variables in R

Lecture 9: Rule for naming a variable

Lecture 10: Data Types and Type-casting

Chapter 3: IMPORTANT: Data Structures in R

Lecture 1: Assignment operator in R

Lecture 2: Theory: Vectors in R

Lecture 3: Access vector items

Lecture 4: Generating sequenced vector

Lecture 5: Vectors in R

Lecture 6: Theory: List in R

Lecture 7: Check if item exists in list

Lecture 8: Add item to the list

Lecture 9: List in R

Lecture 10: Matrices in R

Lecture 11: Relational data

Lecture 12: Data Frames in R

Lecture 13: Theory: Access items from data frame

Lecture 14: Add rows to the data frame

Lecture 15: Add columns to the data frame

Lecture 16: Data Frame in R

Lecture 17: Use data frame

Lecture 18: Factor in R

Chapter 4: Miscellaneous

Lecture 1: Math in R

Lecture 2: Miscellaneous operators in R

Lecture 3: table function in R

Chapter 5: Building Logic in R

Lecture 1: Loops in R

Lecture 2: Theory: Concepts of loops

Lecture 3: while loop in R

Lecture 4: for loop in R

Lecture 5: The apply function in R

Lecture 6: Theory: Function in R

Lecture 7: Functions in R

Lecture 8: Default argument in R

Chapter 6: The "dplyr" package to handle data

Lecture 1: Theory: Introduction to the dplyr package

Lecture 2: Select function in R

Lecture 3: Select function in R

Lecture 4: Filter function in R

Lecture 5: Filter function in R

Lecture 6: Theory: Mutate and Transmute function in R

Lecture 7: Mutate and Transmute function in R

Lecture 8: The diff() function in R

Lecture 9: Theory: Pipe operator in R

Lecture 10: Pipe operator in R (Do not miss this video)

Chapter 7: Introduction to Text mining

Lecture 1: Text Mining in R

Lecture 2: Common Techniques

Lecture 3: Tokenization in R

Lecture 4: Stemming in R

Lecture 5: Natural Language Processing

Lecture 6: Text Mining Applications

Chapter 8: Important Terminologies

Lecture 1: Important Terms in Text Mining

Lecture 2: What is web scraping?

Chapter 9: Project: Sentimental Analysis with R

Lecture 1: Tools for webscraping in R

Lecture 2: Installing rvest package in R

Lecture 3: Read html contents

Lecture 4: Use locator to get html nodes

Lecture 5: Using dplyr

Lecture 6: Data Manipulation

Lecture 7: Change column name

Lecture 8: Get all links

Lecture 9: Cleaning the data

Lecture 10: Clean data continued..

Lecture 11: Filter the data

Lecture 12: Get content using scraping

Lecture 13: Split the data

Lecture 14: Use loops for repeated tasks

Lecture 15: Creating data frame

Lecture 16: Refine the data from data frame

Lecture 17: Count rows and columns

Lecture 18: Theory: What is corpus?

Lecture 19: Theory: Term Document Matrix

Lecture 20: Theory: Bag of Word Models

Lecture 21: Theory: Vector Space Model

Lecture 22: Term Frequency — IMPORTANT

Lecture 23: Inverse Document Frequency model

Lecture 24: Corpus and Term Document Matrix

Lecture 25: Remove Sparse terms

Lecture 26: Frequency distributions

Lecture 27: Theory: Wordclouds in R

Lecture 28: Wordcloud

Lecture 29: Clean the corpus

Lecture 30: Remove stop words

Instructors

Sachin Kafle
Founder of CSAMIN & Bit4Stack Tech Inc. [[Author, Teacher]]

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 0 votes
5 stars: 10 votes

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