Practice Feature Extraction Techniques - 9.4 | 9. Natural Language Processing (NLP) | Data Science Advance
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Academics
Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Professional Courses
Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skillsβ€”perfect for learners of all ages.

games

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the primary focus of the Bag of Words model?

πŸ’‘ Hint: Think about how the model treats words in a document.

Question 2

Easy

Name one advantage of TF-IDF over Bag of Words.

πŸ’‘ Hint: Consider how it weights terms.

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

Which technique focuses on word frequency count and ignores word order?

  • Bag of Words
  • TF-IDF
  • Word Embeddings

πŸ’‘ Hint: Think of a simple counting approach.

Question 2

True or False: TF-IDF gives higher weights to common words across a document.

  • True
  • False

πŸ’‘ Hint: Consider the meaning of 'inverse' in IDF.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a simple document similarity algorithm using Bag of Words and explain how you would implement it.

πŸ’‘ Hint: Think about vector mathematics for similarity.

Question 2

Functionally assess the application of FastText in an NLP system that handles diverse languages - what are the pros and cons?

πŸ’‘ Hint: Consider context and different language structures.

Challenge and get performance evaluation