#NLP

Last updated    - V4 (13-Sep-2025)

Level Topic Subtopics Basic Introduction to NLP What is NLP, History of NLP, Applications, Challenges, NLP vs Computational Linguistics Text Preprocessing Tokenization, Stopwords Removal, Stemming, Lemmatization, Text Cleaning Basic NLP Tasks Language Modeling, Text Classification, Sentiment Analysis, Named Entity Recognition (NER), Part-of-Speech Tagging NLP Tools & Libraries NLTK, SpaCy, Gensim, Hugging Face Transformers, Python NLP Libraries Evaluation Metrics Accuracy, Precision, Recall, F1 Score, BLEU Score, ROUGE, Perplexity Intermediate Embeddings & Representations Word Embeddings (Word2Vec, GloVe), Contextual Embeddings, TF-IDF, Bag-of-Words, One-Hot Encoding Sequence Models Recurrent Neural Networks (RNN), LSTM, GRU, Sequence-to-Sequence Models, Attention Mechanism Advanced NLP Tasks Machine Translation, Question Answering, Text Summarization, Speech-to-Text, Text-to-Speech NLP Pipelines & Workflow Preprocessing Pipelines, Tokenization Strategies, Model Training, Evaluation, Deployment Information Retrieval & Search Vector Space Models, BM25, Embedding-based Retrieval, Semantic Search, Ranking Metrics Advanced Transformer Models Transformers Architecture, BERT, GPT, RoBERTa, T5, Encoder-Decoder Models, Attention Mechanism Contextual & Large Language Models Contextual Embeddings, Pretrained Language Models, Fine-Tuning, Prompt Engineering, Few-Shot/Zero-Shot Learning NLP for Multi-Modal Tasks Text-to-Image, Text-to-Audio, Vision-Language Models, Cross-Modal Learning, Multi-Modal Transformers Optimization & Training Learning Rate Schedulers, Gradient Clipping, Regularization, Mixed Precision, Transfer Learning Advanced Evaluation Perplexity, BLEU, ROUGE, METEOR, Human Evaluation, Error Analysis, Bias Detection Expert Generative NLP Language Generation, Chatbots, GPT-style Models, Reinforcement Learning with Human Feedback (RLHF), Dialogue Systems Explainability & Interpretability Attention Visualization, SHAP, LIME, Counterfactual Explanations, Model Debugging NLP Deployment & MLOps Serving NLP Models, API Integration, Cloud Deployment, Model Monitoring, Continuous Learning Research & Emerging Trends Self-Supervised Learning, Few-Shot / Zero-Shot NLP, Multi-Lingual Models, Retrieval-Augmented Generation, Foundation Models Ethics & Responsible AI Bias & Fairness, Toxicity Detection, Data Privacy, Ethical NLP, Safe AI Deployment

1. NLP Basics

1. What is Natural Language Processing (NLP)?
2. Difference between NLP and computational linguistics.
3. What are some real-world applications of NLP?
4. Explain tokenization and its types.
5. What is stemming and lemmatization?
6. Difference between stemming and lemmatization.
7. What are stopwords and why are they removed?
8. Explain text cleaning techniques.
9. Difference between bag-of-words and TF-IDF.
10. What is part-of-speech tagging?
11. Explain named entity recognition (NER).
12. What is sentiment analysis?
13. Explain language modeling.
14. Difference between unigram, bigram, and n-gram models.
15. What are word embeddings?
16. Explain evaluation metrics: accuracy, precision, recall, F1 score.
17. Difference between supervised and unsupervised NLP tasks.
18. What is perplexity in language models?
19. Difference between lemmatization and normalization.
20. What are common NLP datasets?
21. Explain stopword removal trade-offs.
22. What is text classification?
23. Explain similarity measures in NLP (cosine, Jaccard).
24. What are common Python NLP libraries?
25. How does NLP handle noisy or unstructured text?

---

2. Intermediate NLP

1. Explain Word2Vec embeddings.
2. What is GloVe?
3. Difference between Word2Vec and GloVe.
4. Explain context-free embeddings vs contextual embeddings.
5. What is a recurrent neural network (RNN)?
6. Difference between RNN, LSTM, and GRU.
7. Explain sequence-to-sequence (seq2seq) models.
8. What is attention mechanism?
9. Explain machine translation using NLP.
10. How do you perform text summarization?
11. Explain question answering systems.
12. What is speech-to-text and text-to-speech in NLP?
13. How do you perform semantic search?
14. Explain information retrieval models (vector space, BM25).
15. What is embedding-based retrieval?
16. How do you handle multi-lingual NLP tasks?
17. Explain preprocessing pipelines in NLP.
18. What is entity linking?
19. How do you evaluate NER systems?
20. Explain evaluation metrics for text generation (BLEU, ROUGE).
21. How do you handle out-of-vocabulary (OOV) words?
22. Explain sentence embeddings.
23. Difference between feature-based and fine-tuning approaches for embeddings.
24. How do you perform text clustering?
25. Explain topic modeling (LDA, NMF).

---

3. Advanced NLP

1. Explain transformer architecture.
2. Difference between encoder, decoder, and encoder-decoder models.
3. Explain BERT architecture.
4. What is GPT architecture?
5. Difference between BERT and GPT.
6. Explain RoBERTa and T5 models.
7. How do you fine-tune pre-trained language models?
8. Explain prompt engineering.
9. Difference between few-shot, one-shot, and zero-shot learning.
10. What is retrieval-augmented generation (RAG)?
11. Explain multi-modal NLP (text-to-image, text-to-audio).
12. How do you implement cross-modal learning?
13. Explain transfer learning in NLP.
14. How do you perform regularization in NLP models?
15. Explain gradient clipping in transformer training.
16. Difference between masked language modeling (MLM) and causal language modeling.
17. How do you prevent hallucinations in text generation?
18. Explain evaluation metrics for multi-turn dialogue systems.
19. How do you handle long sequences in transformers?
20. Explain multi-head attention and positional encoding.
21. How do you optimize transformer models for inference?
22. What are embedding spaces and similarity measures?
23. Explain knowledge distillation in NLP.
24. How do you handle bias in NLP models?
25. Explain human evaluation methods for NLP outputs.

---

4. Expert-Level NLP

1. Explain generative NLP models.
2. How do GPT-style models generate text?
3. What is reinforcement learning with human feedback (RLHF)?
4. Explain dialogue systems and chatbots.
5. How do you align LLMs with human intent?
6. Explain foundation models in NLP.
7. How do you deploy NLP models in production?
8. What are best practices for API-based NLP services?
9. Explain continuous learning for NLP systems.
10. How do you monitor deployed NLP models?
11. Explain model drift and data drift.
12. How do you handle multi-lingual and cross-lingual NLP at scale?
13. Explain retrieval-augmented generation at industrial scale.
14. How do you implement privacy-preserving NLP?
15. Explain federated learning in NLP.
16. How do you interpret large language models (LLMs)?
17. Explain SHAP and LIME for NLP models.
18. How do you measure fairness and mitigate bias in NLP?
19. Explain safe and responsible deployment of generative NLP models.
20. How do you combine knowledge graphs with NLP models?
21. Explain multi-agent NLP systems.
22. How do you debug large-scale NLP training pipelines?
23. Explain evaluation metrics for open-ended tasks.
24. What are emerging trends in NLP research?
25. How do you combine retrieval and generation for advanced NLP applications?