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Course Outline

Introduction to NLP

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

Text Preprocessing Techniques

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

Sentiment Analysis

  • Overview of sentiment analysis
  • Conducting sentiment analysis with NLTK
  • Lleveraging SpaCy for advanced sentiment analysis

Advanced NLP Techniques

  • Named entity recognition (NER)
  • Text classification
  • Language modeling using 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
  • Utilizing NLP for chatbots and virtual assistants
  • Emerging trends in NLP research

Summary and Next Steps

Requirements

  • Foundational knowledge of natural language processing concepts
  • Proficiency in Python programming
  • Experience working with Jupyter Notebooks or similar platforms

Target Audience

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

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