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Course Outline

Introduction to NLP

  • What is Natural Language Processing?
  • The significance of NLP in contemporary AI applications
  • Prominent libraries for NLP: NLTK, SpaCy, Hugging Face

Text Preprocessing Techniques

  • Tokenization and removal of stop words
  • Stemming and lemmatization
  • Text normalization techniques

Sentiment Analysis

  • Overview of sentiment analysis
  • Executing sentiment analysis with NLTK
  • Utilising SpaCy for advanced sentiment analysis

Advanced NLP Techniques

  • Named entity recognition (NER)
  • Text classification
  • 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 in Colab

Real-World Applications of NLP

  • Applications of NLP in healthcare, finance, and customer support
  • Leveraging NLP for chatbots and virtual assistants
  • Emerging trends in NLP research

Summary and Next Steps

Requirements

  • Foundational understanding of natural language processing concepts
  • Proficiency in Python programming
  • Experience with Jupyter Notebooks or comparable environments

Audience

  • Data scientists
  • Developers with Python experience
  • AI enthusiasts
 14 Hours

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