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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

bookChallenge: Creating Word Embeddings

Task
test

Swipe to show code editor

Now, it's time for you to train a Word2Vec model to generate word embeddings for the given corpus:

  1. Import the class for creating a Word2Vec model.
  2. Tokenize each sentence in the 'Document' column of the corpus by splitting each sentence into words separated by whitespaces. Store the result in the sentences variable.
  3. Initialize the Word2Vec model by passing sentences as the first argument and setting the following values as keyword arguments, in this order:
    • embedding size: 50;
    • context window size: 2;
    • minimal frequency of words to include in the model: 1;
    • model: skip-gram.
  4. Print the top-3 most similar words to the word 'bowl'.

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Section 4. Chapter 4
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bookChallenge: Creating Word Embeddings

Task
test

Swipe to show code editor

Now, it's time for you to train a Word2Vec model to generate word embeddings for the given corpus:

  1. Import the class for creating a Word2Vec model.
  2. Tokenize each sentence in the 'Document' column of the corpus by splitting each sentence into words separated by whitespaces. Store the result in the sentences variable.
  3. Initialize the Word2Vec model by passing sentences as the first argument and setting the following values as keyword arguments, in this order:
    • embedding size: 50;
    • context window size: 2;
    • minimal frequency of words to include in the model: 1;
    • model: skip-gram.
  4. Print the top-3 most similar words to the word 'bowl'.

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 4. Chapter 4
toggle bottom row

bookChallenge: Creating Word Embeddings

Task
test

Swipe to show code editor

Now, it's time for you to train a Word2Vec model to generate word embeddings for the given corpus:

  1. Import the class for creating a Word2Vec model.
  2. Tokenize each sentence in the 'Document' column of the corpus by splitting each sentence into words separated by whitespaces. Store the result in the sentences variable.
  3. Initialize the Word2Vec model by passing sentences as the first argument and setting the following values as keyword arguments, in this order:
    • embedding size: 50;
    • context window size: 2;
    • minimal frequency of words to include in the model: 1;
    • model: skip-gram.
  4. Print the top-3 most similar words to the word 'bowl'.

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!

Task
test

Swipe to show code editor

Now, it's time for you to train a Word2Vec model to generate word embeddings for the given corpus:

  1. Import the class for creating a Word2Vec model.
  2. Tokenize each sentence in the 'Document' column of the corpus by splitting each sentence into words separated by whitespaces. Store the result in the sentences variable.
  3. Initialize the Word2Vec model by passing sentences as the first argument and setting the following values as keyword arguments, in this order:
    • embedding size: 50;
    • context window size: 2;
    • minimal frequency of words to include in the model: 1;
    • model: skip-gram.
  4. Print the top-3 most similar words to the word 'bowl'.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 4. Chapter 4
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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