NLP Application–Focused Syllabus

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Part I: Foundations

1. Module 1: Linguistic & Computational Foundations
2. Module 2: Text Cleaning & Preprocessing
3. Module 3: Text Representations (BoW, TF–IDF & Embeddings)

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Part II: Core NLP Tasks

4. Module 4: Text Classification & Sentiment Analysis
5. Module 5: Named Entity Recognition & Sequence Labeling
6. Module 6: Topic Modeling & Document Clustering

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Part III: Generation & Summarization

7. Module 7: Language Modeling & Text Generation
8. Module 8: Text Summarization (Extractive & Abstractive)
9. Module 9: Machine Translation & Cross-Lingual Transfer

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Part IV: Information Retrieval & QA

10. Module 10: Document Retrieval & Ranking
11. Module 11: Question Answering & Reading Comprehension
12. Module 12: Dialogue Systems & Chatbots

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Part V: Evaluation, Deployment & Ethics

13. Module 13: Evaluation Metrics for Generation & Classification
14. Module 14: Model Serving & Production Pipelines
15. Module 15: Fairness, Bias & Ethical NLP

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Portfolio Projects (Optional)

• Topic Explorer: LDA + pyLDAvis dashboard
• Summarizer Demo: Extractive vs. Abstractive
• Chatbot App: Transformer-based Q&A
• Translation Tool: Fine-tune on a small bilingual corpus