Data Science: NLP and Sentimental Analysis in R

Go to class
Write Review

Data Science: NLP and Sentimental Analysis in R provided by Udemy is a comprehensive online course, which lasts for 11 hours worth of material. Data Science: NLP and Sentimental Analysis in R is taught by Sachin Kafle. Upon completion of the course, you can receive an e-certificate from Udemy. The course is taught in Englishand is Paid Course. Visit the course page at Udemy for detailed price information.

Overview
  • Learn Natural Language Processing and Sentimental Analysis using "The Big Bang Theory" show script in R.

    What you'll 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

    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.