Natural Language Processing

• – What is NLP?
  – Applications of NLP
  – Tokenization, Stopword Removal, Stemming, and Lemmatization

• – Bag of Words (BoW), TF-IDF
  – Word Embeddings (Word2Vec, GloVe)
  – N-grams, Markov Models, Hidden Markov Models (HMMs)

• – Role of POS tagging in NLP
  – POS tagging algorithms
  – Introduction to NER
  – Rule-based and machine learning-based NER systems

• – Understanding sentiment analysis
  – Sentiment analysis techniques
  – Supervised text classification
  – Building text classifiers using different algorithms

• – Introduction to sequence-to-sequence models
  – Applications of Seq2Seq models
  – Attention mechanisms in NLP applications

• – Deep dive into the Transformer architecture
  – Pre-trained Transformers (e.g., BERT, GPT)
  – Fine-tuning pre-trained Transformers for NLP tasks

• – Introduction to text generation
  – Recurrent Neural Networks (RNNs) for text generation
  – Generating text with RNNs and LSTM
  – In-depth exploration of GPT-3

• – Advanced text classification techniques
  – Building custom NER models
  – Evaluation metrics for NER

– Introduction to Automatic Speech Recognition (ASR)
– Text-to-Speech (TTS) synthesis
– Integrating ASR and TTS into NLP applications

– Ethical considerations in NLP (bias, privacy)
– Recent developments and future trends in NLP
– Student presentations on NLP projects or research