Module 1: Foundations of NLP
- What is NLP? Scope and importance
- Text representation: tokens, vocabulary, corpora
- Basic preprocessing: tokenization, stemming, lemmatization, stopword removal
- Applications overview: chatbots, sentiment analysis, machine translation
Module 2: Text Representation
- Bag-of-Words (BoW) and TF-IDF
- Word embeddings: Word2Vec, GloVe
- Contextual embeddings: ELMo, BERT
- Practical exercise: building a text classifier with TF-IDF
Module 3: Classical NLP Techniques
- Part-of-speech tagging
- Named entity recognition (NER)
- Syntax and parsing (dependency trees, constituency parsing)
- Case study: extracting structured information from text
Module 4: Machine Learning for NLP
- Supervised learning for text classification
- Unsupervised learning for topic modeling (LDA, NMF)
- Sequence models: Hidden Markov Models, CRFs
- Evaluation metrics: BLEU, ROUGE, perplexity
Module 5: Deep Learning in NLP
- Recurrent Neural Networks (RNNs), LSTMs, GRUs
- Attention mechanisms and Transformers
- Pre-trained language models (BERT, GPT, RoBERTa)
- Case study: sentiment analysis with BERT
Module 6: Advanced Applications
- Machine translation (seq2seq, transformers)
- Text summarization (extractive vs. abstractive)
- Question answering systems
- Conversational AI and chatbots
Module 7: Tools and Frameworks
- Python libraries: NLTK, SpaCy, Gensim
- Deep learning frameworks: TensorFlow, PyTorch, Hugging Face Transformers
- Hands-on labs: building NLP pipelines with these tools
Module 8: Challenges and Risks
- Ambiguity and polysemy in language
- Bias in language models
- Low-resource languages and data scarcity
- Scalability and efficiency issues
Module 9: Ethics and Governance
- Responsible NLP practices
- Regulatory frameworks for AI in language applications
- Transparency and explainability in NLP systems
- Building trust in conversational AI
Module 10: Future Trends
- Multilingual and cross-lingual NLP
- Generative AI for creative writing and content production
- NLP in multimodal systems (text + image + audio)
- Integration with knowledge graphs and reasoning engines