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Course Outline

Introduction to NLP

  • What is Natural Language Processing?
  • The importance of NLP in modern AI applications
  • Popular libraries for NLP: NLTK, SpaCy, Hugging Face

Text Preprocessing Techniques

  • Tokenisation and stop word removal
  • Stemming and lemmatisation
  • Text normalisation techniques

Sentiment Analysis

  • Introduction to sentiment analysis
  • Performing sentiment analysis with NLTK
  • Using SpaCy for advanced sentiment analysis

Advanced NLP Techniques

  • Named entity recognition (NER)
  • Text classification
  • Language modelling with pre-trained models

Working with Google Colab

  • Introduction to the Google Colab environment
  • Setting up and managing NLP projects in Colab
  • Collaborating on NLP tasks in Colab

Real-World Applications of NLP

  • NLP in healthcare, finance, and customer support
  • Using NLP for chatbots and virtual assistants
  • Future trends in NLP research

Summary and Next Steps

Requirements

  • A basic understanding of natural language processing concepts
  • Familiarity with Python programming
  • Experience with Jupyter Notebooks or similar environments

Audience

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

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Provisional Upcoming Courses (Require 5+ participants)

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