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Please enable JavaScript in your browser settings or update your browser.Challenge: Creating a Bag of Words | Basic Text Models
Introduction to NLP
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Contenido del Curso

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

bookChallenge: Creating a Bag of Words

Tarea

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 desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 3. Capítulo 5
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bookChallenge: Creating a Bag of Words

Tarea

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 desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 3. Capítulo 5
toggle bottom row

bookChallenge: Creating a Bag of Words

Tarea

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 desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Tarea

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 desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 3. Capítulo 5
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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