Data Science with Python Certification Data Science with Python Natural Language Processing 2

Question 1

Which scikit-learn class converts a collection of text documents to a matrix of TF-IDF features?

Accepted Answer

TfidfVectorizer

Answer

`TfidfVectorizer` combines tokenization, vocabulary building, and TF-IDF scoring into a single transformer compatible with scikit-learn pipelines.

Question 2

What is a bigram in NLP?

Accepted Answer

A sequence of two consecutive words used as a single feature

Answer

A bigram is an n-gram where n=2, capturing pairs of adjacent words (e.g., 'machine learning') to preserve some local word order context.

Question 3

In spaCy, what attribute of a `Token` object returns its part-of-speech tag?

Accepted Answer

token.pos_

Answer

`token.pos_` returns the coarse-grained part-of-speech tag (e.g., 'NOUN', 'VERB') assigned by spaCy's statistical model.

Question 4

What is the purpose of padding sequences when preparing text data for deep learning models?

Accepted Answer

To make all input sequences the same length for batch processing

Answer

Deep learning models require fixed-size inputs; padding (usually with zeros) extends shorter sequences to a uniform length so batches can be processed efficiently.

Question 5

Which method on a scikit-learn vectorizer both learns the vocabulary and transforms the training data in one step?

Accepted Answer

fit_transform()

Answer

`fit_transform()` combines `fit()` (learn vocabulary/IDF) and `transform()` (convert documents to feature matrix) in a single efficient pass over training data.

Data Science with Python Certification Practice Test

Data Science with Python Certification Practice Test

Data Science with Python Certification Data Science with Python Natural Language Processing 2