Get in Touch

Course Outline

Introduction to NLP

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

Text Preprocessing Techniques

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

Sentiment Analysis

  • Overview of sentiment analysis
  • Implementing sentiment analysis with NLTK
  • Leveraging SpaCy for advanced sentiment analysis

Advanced NLP Techniques

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

Working with Google Colab

  • Overview of the Google Colab environment
  • Establishing and managing NLP projects in Colab
  • Collaborating on NLP tasks within Colab

Real-World Applications of NLP

  • NLP implementation in healthcare, finance, and customer support sectors
  • 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
  • Practical experience with Jupyter Notebooks or comparable environments

Audience

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

Related Categories