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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is feature engineering?
💡 Hint: Think about how models use data before training.
Question 2
Easy
Can traditional ML work with a small dataset?
💡 Hint: Consider the amount of data needed for deep learning.
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 feature engineering involve?
- Data cleaning
- Model deployment
- Selecting and transforming variables
- Data visualization
💡 Hint: Focus on the preparation stage before model training.
Question 2
Deep learning is often considered a black box.
- True
- False
💡 Hint: Think about how easy it is to interpret the models.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You are developing a model for a healthcare application. Discuss whether you would choose traditional ML or deep learning and why, considering interpretability and data availability.
💡 Hint: Think about the regulatory environment in healthcare.
Question 2
Evaluate the impact of deep learning in real-time applications, like self-driving cars, where data is continuously generated.
💡 Hint: Consider how data volume challenges traditional methods.
Challenge and get performance evaluation