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خانه » فروشگاه » Complete Guide to NLP with R

Complete Guide to NLP with R

سرفصل های دوره

Natural Language Processing is to words as Computer Vision is to pictures! Learn NLP with the R programming language. In this course, experienced technologist Mark Niemann-Ross shows you how to use the R programming language to implement natural language processing algorithms. R is uniquely adept at manipulating matrices and producing statistics, both of which are core to NLP. Learn about frameworks that you can use with NLP, as well as the importance of corpora and sources. Find out how to work with NLP metadata and preprocess text in preparation for NLP. Explore creating structured data, applying statistics to text, and performing sentiment analysis, and then dive into visualizing NLP. Discover ways to use tidytext and quanteda R for NLP. Build your understanding of corpora, tokens, and document-feature matrix (DFM). Plus, go over analysis and visualization.

This course was created by Mark Niemann-Ross. We are pleased to host this training in our library.


01 - Introduction
  • 01 - Welcome to natural language processing with R
  • 02 - Skills and tools you need to be successful in this course

    • 02 - 1. Up and Running with tm
  • 01 - What is tm and why do you need it
  • 02 - Real-world NLP with tm
  • 03 - Real-world NLP with quanteda
  • 04 - Real-world NLP with tidytext

    • 03 - 2. Corpora and Sources
  • 01 - Understanding corpora and sources
  • 02 - Examining corpora
  • 03 - Examining sources
  • 04 - Custom sources
  • 05 - Combining and subsetting corpora

    • 04 - 3. Working with NLP Metadata
  • 01 - Working with document metadata
  • 02 - Make useful metadata
  • 03 - Finding and filtering based on metadata

    • 05 - 4. Preprocessing Text in Preparation for NLP
  • 01 - Transformations
  • 02 - Stop words
  • 03 - Stemming
  • 04 - Lemmatization
  • 05 - Tokenization
  • 06 - N-grams
  • 07 - Part of speech tagging

    • 06 - 5. Create Structured Data
  • 01 - Understanding the document-term matrix
  • 02 - Create the document-term matrix
  • 03 - Weighting the document-term matrix
  • 04 - Focus the document-term matrix

    • 07 - 6. Apply Statistics to Text
  • 01 - Word and document frequency
  • 02 - Hierarchical clustering
  • 03 - Associated terms

    • 08 - 7. Sentiment Analysis
  • 01 - What is sentiment analysis
  • 02 - Real-world example of sentiment analysis
  • 03 - Sentiment datasets
  • 04 - Sentiment tools

    • 09 - 8. Visualizing Natural Language Processing
  • 01 - Plotting text mining
  • 02 - Plotting Zipfs and Heaps Law
  • 03 - Word clouds

    • 10 - 9. Conclusion
  • 01 - Your next steps in NLP

    • 11 - 10. Introduction to NLP Tidytext R
  • 01 - Welcome to natural language processing with R
  • 02 - Skills you need to be successful in this course

    • 12 - 11. Use of Tidytext for NLP
  • 01 - How to think like tidytext
  • 02 - An example Calculate the most popular terms in a document
  • 03 - Tokenizing with unnest tokens( )
  • 04 - Stopwords, punctuation, whitespace, and numbers
  • 05 - Stemming and lemmatization
  • 06 - Term frequency with bind tf idf( )
  • 07 - Sentiment analysis with sentiments( )
  • 08 - Parts of speech with parts of speech( )
  • 09 - Import and export from other NLP packages

    • 13 - 12. Conclusion
  • 01 - Next steps

    • 14 - 13. Introduction to NLP with Quanteda R
  • 01 - Welcome to natural language processing with R
  • 02 - Skills and tools you need

    • 15 - 14. Getting Started with Quanteda
  • 01 - Introduction to quanteda
  • 02 - Install quanteda

    • 16 - 15. Understanding Corpora
  • 01 - Create a quanteda corpus
  • 02 - Create metadata with docvars
  • 03 - Corpus subsets and groups
  • 04 - Reshape and segment a corpus
  • 05 - Remove lines from a corpus

    • 17 - 16. Understanding Tokens
  • 01 - Corpus and tokens
  • 02 - Remove tokens and stopwords
  • 03 - Group tokens
  • 04 - Stemming with tokens

    • 18 - 17. Understanding Document-Feature Matrix (DFM)
  • 01 - Corpus, tokens, and DFM
  • 02 - Create and modify a DFM
  • 03 - Real-world analysis with DFM

    • 19 - 18. Analysis and Visualization
  • 01 - The quanteda textstats package
  • 02 - Real-world text statistics with textstats
  • 03 - Understand the quanteda sentiment package
  • 04 - Real-world sentiment analysis with quanteda sentiment
  • 05 - Visualization with textplots
  • 06 - Use dplyr with quanteda

    • 20 - 19. Conclusion
  • 01 - Your next steps in NLP

    • 21 - 20. Capstone Project
  • 01 - Project introduction
  • 02 - Project explanation
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    تاریخ انتشار: ۲۱ آذر ۱۴۰۳
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