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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is tokenization?
💡 Hint: Think about how you can break a sentence into words.
Question 2
Easy
Name one example of a stopword.
💡 Hint: These are common words often removed in preprocessing.
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 does tokenization achieve?
- Removes common words
- Splits text into tokens
- Reduces words to root form
💡 Hint: Think about the action done on the text.
Question 2
True or False: Stopword removal only focuses on nouns.
- True
- False
💡 Hint: Consider if stopwords include other types of words.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given the text 'The quick brown fox jumps over the lazy dog', write Python code to tokenize, remove stopwords, and apply stemming.
💡 Hint: Consider which libraries you need for each step in Python.
Question 2
Explain how you would evaluate the effectiveness of your text preprocessing on a machine learning model.
💡 Hint: Think about comparing model performance with and without preprocessing steps.
Challenge and get performance evaluation