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      • NLTK with Python 3 for Natural Language Processing

      NLTK with Python 3 for Natural Language Processing

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      ITET Admin
      Artificial Intelligence, OS
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      NLTK
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      Natural Language Processing is a field of computer science that focuses on the interaction between computers and humans. NLP techniques are used to analyze text, providing a way for computers to understand human language. A few examples of NLP applications include automatic summarization, topic segmentation, and sentiment analysis.

      NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries.

      This NLTK toolkit is one of the most powerful NLP libraries which contains packages to make machines understand human language and reply to it with an appropriate response. Tokenization, Stemming, Lemmatization, Punctuation, Character count, word count are some of these packages which will be discussed in this course.

      This course will provide an introduction to NLTK  with Python for Natural Language Processing.

      Course Features

      • Lectures 21
      • Quizzes 0
      • Duration 20 hours
      • Skill level All levels
      • Language English
      • Students 17
      • Assessments Yes
      ITET CoursesOSNLTK with Python 3 for Natural Language Processing
      • Chapter 1 4

        • Lecture1.1
          Tokenizing words and Sentences 20 min
        • Lecture1.2
          Stop Words 08 min
        • Lecture1.3
          Stemming 09 min
        • Lecture1.4
          Part of Speech Tagging 10 min
      • Chapter 2 4

        • Lecture2.1
          Chunking 14 min
        • Lecture2.2
          Chinking 06 min
        • Lecture2.3
          Named Entity Recognition 07 min
        • Lecture2.4
          Lemmatizing 05 min
      • Chapter 3 4

        • Lecture3.1
          NLTK Corpora 11 min
        • Lecture3.2
          WordNet 15 min
        • Lecture3.3
          Text Classification 12 min
        • Lecture3.4
          Words as Features for Learning 08 min
      • Chapter 4 5

        • Lecture4.1
          Naive Bayes 09 min
        • Lecture4.2
          Save Classifier with Pickle 07 min
        • Lecture4.3
          Scikit-Learn incorporation 17 min
        • Lecture4.4
          Combining Algos with a Vote 16 min
        • Lecture4.5
          Investigating Bias 11 min
      • Chapter 5 4

        • Lecture5.1
          Better training data 18 min
        • Lecture5.2
          Sentiment Analysis Module 10 min
        • Lecture5.3
          Twitter Sentiment Analysis 13 min
        • Lecture5.4
          Graphing Live Twitter Sentiment 09 min
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