Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language. This course covers the fundamentals of NLP, including text preprocessing, feature extraction, and machine learning models for text classification, sentiment analysis, and more.

Course Outline

  • Introduction to NLP

    • What is NLP?
    • Importance of NLP in AI
    • Overview of NLP applications

  • Text Preprocessing

    • Tokenization
    • Stop words removal
    • Stemming and lemmatization

  • Feature Extraction

    • Bag of Words (BoW)
    • Term Frequency-Inverse Document Frequency (TF-IDF)
    • Word Embeddings

  • Machine Learning Models for NLP

    • Naive Bayes
    • Support Vector Machines (SVM)
    • Recurrent Neural Networks (RNN)
    • Transformers

Learning Outcomes

  • Understand the basics of NLP and its applications
  • Learn to preprocess text data
  • Implement feature extraction techniques
  • Apply machine learning models to NLP tasks

Additional Resources

For further reading, check out our Introduction to Machine Learning course.

NLP in Action