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.

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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.