Natural Language Processing (NLP)

Detailed syllabus for the NLP course.

• Introduction to NLP
  • Overview of natural language processing and applications
  • Challenges in NLP and linguistic nuances
• Text Preprocessing and Cleaning
  • Tokenization, stemming, and lemmatization
  • Stop word removal, normalization, and punctuation handling
• Text Representation Techniques
  • Bag of Words, TF-IDF, and word embeddings
  • Introduction to Word2Vec and GloVe
• Building NLP Models
  • Implementing text classification and sentiment analysis
  • Sequence modeling with RNNs and LSTMs
• Advanced NLP Techniques
  • Introduction to transformers and BERT
  • Transfer learning in NLP with pre-trained models