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Course Outline

Overview of Natural Language Processing

  • Definition and scope of Natural Language Processing
  • Strategic value of NLP in contemporary artificial intelligence solutions for government
  • Key software libraries: NLTK, SpaCy, Hugging Face

Text Preprocessing Methodologies

  • Tokenization and exclusion of stop words
  • Stemming and lemmatization processes
  • Techniques for text normalization

Sentiment Analysis Frameworks

  • Fundamentals of sentiment analysis
  • Implementation of sentiment analysis using NLTK
  • Application of SpaCy for enhanced sentiment evaluation

Advanced NLP Capabilities

  • Named entity recognition (NER)
  • Text classification methodologies
  • Language modeling utilizing pre-trained architectures

Utilizing Google Colab Environments

  • Overview of the Google Colab platform
  • Configuration and management of NLP initiatives in Colab
  • Facilitating collaborative NLP workflows in Colab for government teams

Practical Applications of NLP

  • Deployment in healthcare, financial services, and customer support sectors
  • Development of chatbots and virtual assistance systems
  • Emerging trends in NLP research and development

Summary and Strategic Next Steps

Requirements

  • Foundational knowledge of natural language processing principles
  • Competence in Python programming
  • Proficiency with Jupyter Notebooks or comparable development environments

Audience

  • Data scientists
  • Developers with Python expertise
  • Artificial intelligence practitioners seeking tools for government applications
 14 Hours

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