ML Interview Questions

In the context of Natural Language Processing (NLP), how is transfer learning applied? Discuss its benefits and provide examples of models or techniques that utilize transfer learning effectively.

Explain the difference between stemming and lemmatization in Natural Language Processing (NLP). Provide examples of how each is used in practice and discuss any advantages or disadvantages they may have.

What is Named Entity Recognition (NER), and what are some of the common approaches used to tackle this task in Natural Language Processing? Discuss the role of NER in Information Extraction and how it relates to sequence labeling.

Describe the attention mechanism and discuss its significance in the architecture of Transformer models for NLP tasks.

Explain BPE, WordPiece, and other subword tokenization methods and their advantages in Natural Language Processing.

How do you handle out-of-vocabulary (OOV) words in natural language processing systems, and what are some techniques to address this issue effectively?

Describe the evolution of sentiment analysis techniques from rule-based systems to deep learning methods, highlighting their theoretical foundations and practical applications.

Explain BERT's architecture, pretraining objectives, and fine-tuning process.

What are word embeddings, and how do models like Word2Vec and GloVe generate these embeddings? Discuss their differences and potential use cases in Natural Language Processing (NLP).

Explain the sequence-to-sequence (seq2seq) model and discuss its structure, working mechanism, and possible applications in NLP.