Practice - Data Preprocessing and Feature Engineering
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Practice Questions
Test your understanding with targeted questions
What is data cleaning?
💡 Hint: Think about why clean data is critical.
Why do we normalize data?
💡 Hint: Consider how different units could affect learning.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is the primary purpose of data cleaning?
💡 Hint: Consider the initial step before any model training.
True or False: Feature engineering is unnecessary if the dataset is large.
💡 Hint: Think about how features impact model performance.
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Challenge Problems
Push your limits with advanced challenges
Given a dataset with numerous missing values and outliers, outline a detailed plan to preprocess this data for training a machine learning model.
💡 Hint: Break it down into cleaning, engineering features, and then scaling.
How can poor feature engineering lead to the failure of an AI application? Provide an example.
💡 Hint: Consider how representation of data informs learning.
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Reference links
Supplementary resources to enhance your learning experience.