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Introduction to NLP
course content

Course Content

Introduction to NLP

Introduction to NLP

1. Text Preprocessing Fundamentals
NLP and Its ApplicationsUnderstanding Text PreprocessingTokenizationChallenge: Tokenizing a Sentence Tokenization Using Regular ExpressionsChallenge: Tokenizing Using RegexRemoving Stop WordsApplying Text Preprocessing in Practice
2. Stemming and Lemmatization
StemmingChallenge: Stemming the TokensLemmatizationPart of Speech TaggingLemmatization with POS Tagging
3. Basic Text Models
Overview of Vector Space ModelsKey Types of Vector Space ModelsBag of WordsCustomizing Bag of WordsChallenge: Creating a Bag of WordsTF-IDFImplementing TF-IDF
4. Word Embeddings
Word Embeddings BasicsCBoW and Skip-gram Models (Optional)Implementing Word2VecChallenge: Creating Word Embeddings

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Challenge: Creating a Bag of Words

Task
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Your task is to display the vector for the 'graphic design' bigram in a BoW model:

  1. Import the CountVectorizer class to create a BoW model.

  2. Instantiate the CountVectorizer class as count_vectorizer, configuring it for a frequency-based model that includes both unigrams and bigrams.

  3. Utilize the appropriate method of count_vectorizer to generate a BoW matrix from the 'Document' column in the corpus.

  4. Convert bow_matrix to a dense array and create a DataFrame from it, setting the unique features (unigrams and bigrams) as its columns. Assign this to the variable bow_df.

  5. Display the vector for 'graphic design' as an array, rather than as a pandas Series.

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Section 3. Chapter 5
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book
Challenge: Creating a Bag of Words

Task
test

Swipe to show code editor

Your task is to display the vector for the 'graphic design' bigram in a BoW model:

  1. Import the CountVectorizer class to create a BoW model.

  2. Instantiate the CountVectorizer class as count_vectorizer, configuring it for a frequency-based model that includes both unigrams and bigrams.

  3. Utilize the appropriate method of count_vectorizer to generate a BoW matrix from the 'Document' column in the corpus.

  4. Convert bow_matrix to a dense array and create a DataFrame from it, setting the unique features (unigrams and bigrams) as its columns. Assign this to the variable bow_df.

  5. Display the vector for 'graphic design' as an array, rather than as a pandas Series.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 3. Chapter 5
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