Introduction to NLP Using R

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Mark Niemann-Ross

2:32:05

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01 - Introduction

01 - Welcome to natural language processing with R.mp4

01:35

02 - Skills and tools youll need to be successful in this course.mp4

02:25

02 - 1. Up and Running with tm

01 - What is tm and why do you need it.mp4

02:18

02 - tm documentation walk-through.mp4

02:52

03 - Real-world NLP with tm.mp4

05:15

04 - Real-world NLP with quanteda.mp4

04:17

05 - Real-world NLP with tidytext.mp4

03:28

03 - 2. Corpora and Sources

01 - Understanding corpora and sources.mp4

02:16

02 - Examining corpora.mp4

07:06

03 - Examining sources.mp4

07:36

04 - Custom sources.mp4

02:18

05 - Combining and subsetting corpora.mp4

05:07

04 - 3. Working with NLP Metadata

01 - Working with document metadata.mp4

08:28

02 - Make useful metadata.mp4

06:04

03 - Finding and filtering based on metadata.mp4

05:28

05 - 4. Preprocessing Text in Preparation for NLP

01 - Transformations.mp4

03:52

02 - Stop words.mp4

03:54

03 - Stemming.mp4

03:38

04 - Lemmatization.mp4

03:24

05 - Tokenization.mp4

03:54

06 - Ngrams.mp4

06:32

07 - Part of speech tagging.mp4

04:51

06 - 5. Create Structured Data

01 - Understanding the document-term matrix.mp4

03:44

02 - Create the document-term matrix.mp4

05:29

03 - Weighting the document-term matrix.mp4

03:56

04 - Focus the document-term matrix.mp4

04:13

07 - 6. Apply Statistics to Text

01 - Word and document frequency.mp4

03:29

02 - Hierarchical clustering.mp4

03:20

03 - Associated terms.mp4

03:55

08 - 7. Sentiment Analysis

01 - What is sentiment analysis.mp4

02:40

02 - Real-world example of sentiment analysis.mp4

05:49

03 - Sentiment datasets.mp4

05:02

04 - Sentiment tools.mp4

03:02

09 - 8. Visualizing Natural Language Processing

01 - Plotting text mining.mp4

04:09

02 - Plotting Zipfs and Heaps Law.mp4

02:12

03 - Word clouds.mp4

02:15

10 - Conclusion

01 - Your next steps in NLP.mp4

02:12

Description

Natural language processing (NLP) is one of the most important components of artificial intelligence. It allows you to process, analyze, and understand large amounts of data in the form of natural language. In this course, instructor Mark Niemann-Ross shows you how to get started implementing NLP algorithms using R, the popular programming language for statistical computing and graphics.

Explore the basics of manipulating matrices and producing statistics, both of which are core to successful NLP. Learn how to use tools and text-mining frameworks such as tm, quanteda, and tidytext, as well as work with corpora, sources, and other types of NLP document metadata. Mark covers the best practices for preprocessing text in preparation for NLP, creating structured data, applying statistics to text, performing sentiment analysis, visualizing datasets, and more.

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Machine Learning

Artificial Intelligence

R Programming

Mark Niemann-Ross

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I’m friends with technology. But I’m not blindly in love with it. I ask it questions. I explain it. I prioritize people over technology. Bigger, better, and faster features make shinier products, but that isn’t what people want. People don’t want widgets, they want elegance and relevance. I’m an educator, manager, evangelist, marketer, technologist, futurist, coder. I point to the future. I explain the present. I learn from the past. People speak different languages: marketing, sales, engineering, management, strategy. I’m fluent in all of those. Sometimes I’m abrupt. Sometimes I apologize. I’m passionate, but willing to believe I’m wrong. I try to inform myself. “Why” is my favorite question, followed by “When.” I write science fiction. Sometimes it’s about spaceships, sometimes it’s about products. The goal is the same; explain where we want to be, point out hazards, celebrate arrival. I’m engaging. Informed. Opinionated. Respectful. Your thoughts?

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