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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does SVD stand for?
💡 Hint: Think about matrix decomposition and factorization in linear algebra.
Question 2
Easy
What library do we use to build recommender systems in Python?
💡 Hint: It has 'surprise' in its name.
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 percentage of the dataset is typically used for training?
- 50%
- 70%
- 80%
- 90%
💡 Hint: Think about common practices in data science.
Question 2
True or False: RMSE can be used to measure the accuracy of predictions.
- True
- False
💡 Hint: Consider the definition of RMSE.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You have a movie dataset. Propose how you would structure your recommendation system using collaborative filtering.
💡 Hint: Think about data attributes, algorithm choices, and user patterns.
Question 2
Create a Python function that takes the user-item matrix, applies SVD, and returns the predicted ratings.
💡 Hint: Focus on the dataset initialization and model fitting process.
Challenge and get performance evaluation