Natural Language Processing (NLP) Overview — ML Technical Interview Questions & Answers

Teaching machines to understand and generate human language.

Natural Language Processing (NLP) Overview

  • Simple meaning: NLP = machine learning (and linguistics) on language: spam, translate, search, chat. Text is not a number until you turn it into features (bag-of-words, TF-IDF, embeddings).
  • Picture: Count “loan” “urgent” in emails → Naive Bayes. Modern chat uses transformers (token → vectors → next token).
  • Fresher path: sklearn TfidfVectorizer + logistic/NB. Then know that BERT-style models exist.
  • Trap: feeding raw strings into LinearRegression. Trap: ignoring language-specific tokenisation.

Technical Interview Tip: Text → vectors → classifier. TF-IDF baseline, then transformers.

Code Examples for Natural Language Processing (NLP) Overview

Natural Language Processing (NLP) Overview — working example

# PCA-style intuition: project 2D → 1D by a direction vector
points = [(1.0, 1.0), (2.0, 2.0), (3.0, 2.8)]
# unit direction along y = x
ux, uy = 0.7071, 0.7071
proj = [x * ux + y * uy for x, y in points]
print([round(v, 3) for v in proj])

# Tiny ensemble: average two model votes
votes = [1, 0, 1]
v2 = [1, 1, 1]
ens = [1 if a + b >= 2 else 0 for a, b in zip(v1, v2)]
print(ens)

Natural Language Processing (NLP) Overview — dim reduction compresses features; ensembles combine models; deep nets/RL/NLP are specialized families — practice the core math idea first.

How to Explain Natural Language Processing (NLP) Overview to an Interviewer

How do you start NLP classically?

Bag-of-words or TF-IDF Then NB/logistic

Embeddings?

Vectors for tokens/sentences Used by modern nets