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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 PreprocessingTokenizationTokenize a Sentence Tokenization Using Regular ExpressionsTokenize Using RegexRemoving Stop WordsApplying Text Preprocessing in Practice
2. Stemming and Lemmatization
StemmingStem 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 WordsCreate a Bag of WordsTF-IDFImplementing TF-IDF
4. Word Embeddings
Word Embeddings BasicsCBoW and Skip-gram Models (Optional)Implementing Word2VecCreate Word Embeddings

Create 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.

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.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 3. Capítulo 5
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Create 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.

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.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 3. Capítulo 5
toggle bottom row

Create 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.

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.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

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.

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