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 Duration 14 hours

Course Outline

Introduction to NLP

  • Defining Natural Language Processing
  • The role of NLP in contemporary AI applications
  • Key NLP libraries: NLTK, SpaCy, and Hugging Face

Text Preprocessing Methods

  • Tokenization and filtering stop words
  • Applying stemming and lemmatization
  • Techniques for text normalization

Sentiment Analysis

  • Overview of sentiment analysis concepts
  • Implementing sentiment analysis with NLTK
  • Advanced sentiment analysis using SpaCy

Advanced NLP Techniques

  • Named Entity Recognition (NER)
  • Text classification strategies
  • Language modeling with pre-trained models

Working with Google Colab

  • Navigating the Google Colab environment
  • Configuring and managing NLP projects in Colab
  • Collaborative NLP development in Colab

Practical Applications of NLP

  • NLP in healthcare, finance, and customer service
  • Developing chatbots and virtual assistants with NLP
  • Emerging trends in NLP research

Recap and Future Steps

Requirements

  • Foundational knowledge of natural language processing concepts
  • Proficiency in Python programming
  • Prior experience with Jupyter Notebooks or comparable development environments

Target Audience

  • Data scientists
  • Developers with a background in Python
  • Enthusiasts of Artificial Intelligence

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