Course
Introduction to Natural Language Processing in R
IntermediateSkill Level
Updated 05/2024RMachine Learning4 hr15 videos47 Exercises3,750 XP8,508Statement of Accomplishment
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Prerequisites
Intermediate RIntroduction to the Tidyverse1
True Fundamentals
Chapter 1 of Introduction to Natural Langauge Processing prepares you for running your first analysis on text. You will explore regular expressions and tokenization, two of the most common components of most analysis tasks. With regular expressions, you can search for any pattern you can think of, and with tokenization, you can prepare and clean text for more sophisticated analysis. This chapter is necessary for tackling the techniques we will learn in the remaining chapters of this course.
2
Representations of Text
In this chapter, you will learn the most common and studied ways to analyze text. You will look at creating a text corpus, expanding a bag-of-words representation into a TFIDF matrix, and use cosine-similarity metrics to determine how similar two pieces of text are to each other. You build on your foundations for practicing NLP before you dive into applications of NLP in chapters 3 and 4.
3
Applications: Classification and Topic Modeling
Chapter 3 focuses on two common text analysis approaches, classification modeling, and topic modeling. If you are working on text analysis projects, you will inevitably use one or both of these methods. This chapter teaches you how to perform both techniques and provides insight into how to approach these techniques from a practical point of you.
4
Advanced Techniques
In chapter 4 we cover two staples of natural language processing, sentiment analysis, and word embeddings. These are two analysis techniques that are a must for anyone learning the fundamentals of text analysis. Furthermore, you will briefly learn about BERT, part-of-speech tagging, and named entity recognition. Almost 15 different analysis techniques were covered in this course, so chapter 4 ends by recapping all of the great techniques you will learn about in this course.
Introduction to Natural Language Processing in R
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FAQs
What NLP techniques are introduced in this R course?
You will learn regular expressions, tokenization, bag-of-words, TF-IDF, cosine similarity, text classification, topic modeling, sentiment analysis, word embeddings, and named entity recognition.
Do I need machine learning experience before starting?
No. This is a beginner-level course that only requires Intermediate R and familiarity with the tidyverse. NLP and machine learning concepts are introduced from scratch.
Does the course cover modern NLP models like BERT?
The course briefly introduces BERT and part-of-speech tagging in the final chapter, but the primary focus is on foundational techniques like bag-of-words, TF-IDF, and topic modeling.
What practical applications will I learn to build?
You will build text classification models and topic models in Chapter 3, and perform sentiment analysis and work with word embeddings in Chapter 4.
How long does this course take to complete?
The course has 4 chapters with 47 exercises. Most learners complete it in about 4 to 5 hours based on median and average completion times.
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