Text Mining and NLP using R and Python

Text Mining and NLP using R and Python, available at $19.99, has an average rating of 3.5, with 34 lectures, 1 quizzes, based on 139 reviews, and has 1592 subscribers.

You will learn about Perform text mining applications using structured & unstructured data; Understand about document term matrix, term frequency, term frequency inverse document, term frequency for normalizing Differentiate between size of word which indicates the frequency of the said word in a word cloud, clustering based on related use for better insights and how to read the results in context to make sense of the word Understand from a practical case study the various steps of text mining in R and the use of Positive and negative word banks Learn Web and Social media extraction using R, Risk sensing – sentiment analysis, Twitter application management for extracting tweets Understand the clustering concept, that is an integral part of text mining This course is ideal for individuals who are All the IT professionals, whose experience ranges from 0 onwards are eligible to take this session. Especially professionals from data analysis, data warehouse, data mining, business intelligence, reporting, data science, etc, will naturally fit in well to take this course. It is particularly useful for All the IT professionals, whose experience ranges from 0 onwards are eligible to take this session. Especially professionals from data analysis, data warehouse, data mining, business intelligence, reporting, data science, etc, will naturally fit in well to take this course.

Enroll now: Text Mining and NLP using R and Python

Summary

Title: Text Mining and NLP using R and Python

Price: $19.99

Average Rating: 3.5

Number of Lectures: 34

Number of Quizzes: 1

Number of Published Lectures: 29

Number of Published Quizzes: 1

Number of Curriculum Items: 35

Number of Published Curriculum Objects: 30

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Perform text mining applications using structured & unstructured data;

Understand about document term matrix, term frequency, term frequency inverse document, term frequency for normalizing

Differentiate between size of word which indicates the frequency of the said word in a word cloud, clustering based on related use for better insights and how to read the results in context to make sense of the word

Understand from a practical case study the various steps of text mining in R and the use of Positive and negative word banks

Learn Web and Social media extraction using R, Risk sensing – sentiment analysis, Twitter application management for extracting tweets

Understand the clustering concept, that is an integral part of text mining

Who Should Attend

All the IT professionals, whose experience ranges from 0 onwards are eligible to take this session. Especially professionals from data analysis, data warehouse, data mining, business intelligence, reporting, data science, etc, will naturally fit in well to take this course.

Target Audiences

All the IT professionals, whose experience ranges from 0 onwards are eligible to take this session. Especially professionals from data analysis, data warehouse, data mining, business intelligence, reporting, data science, etc, will naturally fit in well to take this course.

During this course you will be introduced to one of the most important and fast catching up data mining concept. The need for making sense of unstructured data and the knowledge of the various tools is of paramount importance.

1. Text mining is the first step in data mining of unstructured data.

2. As part of this course you will be introduced to the various stages of text mining

3. Understand about word cloud, clustering, and making analysis based on context,

4. Use of Negative and positive words banks for relational analysis

5. Work with a live example of extraction of data from Web and perform all the facets of text mining using R and Python

6. Learn Web and Social media extraction using R, Risk sensing – sentiment analysis, Twitter application management for extracting tweets

Course Curriculum

Chapter 1: Text Mining Introduction

Lecture 1: Introduction,Importance and Bag Of Words Representation

Lecture 2: Terminology And Preprocessing Of Data

Lecture 3: DTM And TDM Format

Lecture 4: Corpus Level Word Cloud

Lecture 5: Case Study On Real Project Part- 1

Lecture 6: Case Study On Real Project Part- 2

Chapter 2: Text Analytics Using R

Lecture 1: Twitter Data Extraction Using R

Lecture 2: Amazon Data Extraction Using R

Lecture 3: Recap Text Mining Attached With Assignment

Lecture 4: Bonus Section.Whats Next..?

Chapter 3: Optional Topic of Data Analytics

Lecture 1: Hierarchical Clustering Introduction

Lecture 2: K Means Clustering Introduction

Lecture 3: Whats Next..?

Chapter 4: Natural Language Processing

Lecture 1: NLP-Introduction

Lecture 2: Text Analytics – LDA and Lexicons

Lecture 3: Text Analytics – Data Cleansing_Rprogram

Lecture 4: Text Analytics – Wordcloud

Lecture 5: Text Analytics – advance wordclouds & Emotion mining

Lecture 6: NLP_R program-Data cleansing

Lecture 7: NLP_R program-LDA

Lecture 8: NLP_R program-LDA and Sentiment Vector

Lecture 9: NLP-Polarity Plots

Lecture 10: NLP-Emotion mining

Lecture 11: NLP-Named-Entity Recognition

Chapter 5: Text Mining Using Python

Lecture 1: Introduction – Python 3.5

Lecture 2: Python – libraries for text mining

Lecture 3: Data extraction – Amazon Reviews Extraction

Lecture 4: Data Cleansing & Wordcloud

Lecture 5: Positive and Negative Wordclouds

Instructors

ExcelR Solutions
Pioneer in professional management trainings & consulting

Rating Distribution

1 stars: 12 votes

2 stars: 10 votes

3 stars: 23 votes

4 stars: 30 votes

5 stars: 64 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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