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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does vectorization do in NLP?
💡 Hint: Think about how computers understand language.
Question 2
Easy
Name one method of vectorization.
💡 Hint: It's a way to assess word importance.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the purpose of vectorization in NLP?
- To convert numbers into text
- To analyze language
- To convert text into numerical vectors
💡 Hint: Think about how computers process data.
Question 2
TF-IDF emphasizes common words in documents.
- True
- False
💡 Hint: Consider the purpose of TF-IDF.
Solve 3 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Consider a document set containing a lot of reviews for different products. How might you use TF-IDF to prioritize key features highlighted in the reviews?
💡 Hint: Focus on words that appear frequently in certain documents but are rare across the entire set.
Question 2
Design a small experiment comparing the effectiveness of Word2Vec and GloVe embeddings in a sentiment analysis task. What factors would you consider?
💡 Hint: Think about how word meanings could affect sentiment classification.
Challenge and get performance evaluation