Course Outline

Introduction to Natural Language Processing (NLP)

  • Overview of Natural Language Processing
  • Significance of NLP in contemporary artificial intelligence applications for government
  • Prominent libraries for NLP: NLTK, SpaCy, and Hugging Face

Text Preprocessing Techniques

  • Tokenization and removal of stop words
  • Stemming and lemmatization processes
  • Methods for text normalization

Sentiment Analysis

  • Overview of sentiment analysis
  • Conducting sentiment analysis using NLTK
  • Utilizing SpaCy for advanced sentiment analysis for government applications

Advanced NLP Techniques

  • Named entity recognition (NER)
  • Text classification methods
  • Language modeling with pre-trained models for enhanced accuracy and efficiency

Working with Google Colab

  • Introduction to the Google Colab environment for government use
  • Setting up and managing NLP projects in Colab for government
  • Collaborating on NLP tasks within the Colab platform

Real-World Applications of NLP

  • Utilization of NLP in healthcare, finance, and customer support for government operations
  • Implementing NLP for chatbots and virtual assistants to improve public services
  • Emerging trends in NLP research and their implications for government

Summary and Next Steps

Requirements

  • Basic understanding of natural language processing concepts for government applications
  • Familiarity with Python programming
  • Experience with Jupyter Notebooks or similar development environments

Audience

  • Data scientists working in government agencies
  • Developers with experience in Python for government projects
  • AI enthusiasts interested in public sector applications
 14 Hours

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