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Course Outline

Introduction to NLP

  • Defining Natural Language Processing.
  • The significance of NLP in contemporary AI applications.
  • Leading libraries for NLP: NLTK, SpaCy, and Hugging Face.

Text Preprocessing Techniques

  • Tokenization and the removal of stop words.
  • Stemming and lemmatization processes.
  • Various text normalization techniques.

Sentiment Analysis

  • Overview of sentiment analysis.
  • Conducting sentiment analysis with NLTK.
  • Utilizing SpaCy for advanced sentiment analysis.

Advanced NLP Techniques

  • Named Entity Recognition (NER).
  • Text classification methods.
  • Language modeling with pre-trained models.

Working with Google Colab

  • Overview of the Google Colab environment.
  • Setting up and managing NLP projects in Colab.
  • Collaborating on NLP tasks within Colab.

Real-World Applications of NLP

  • NLP implementations in healthcare, finance, and customer support sectors.
  • Leveraging NLP for chatbots and virtual assistants.
  • Emerging trends in NLP research.

Summary and Next Steps

Requirements

  • A foundational understanding of natural language processing concepts.
  • Proficiency in Python programming.
  • Prior experience with Jupyter Notebooks or comparable development environments.

Target Audience

  • Data scientists.
  • Developers with a strong background in Python.
  • Enthusiasts of Artificial Intelligence.
 14 Hours

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