6.2.2 - Principal Component Analysis (PCA)
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Practice Questions
Test your understanding with targeted questions
What does PCA stand for?
💡 Hint: Think about what 'P' stands for.
What is the first step in PCA?
💡 Hint: Consider what we often do to data before analyzing.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is the purpose of PCA?
💡 Hint: Remember, PCA simplifies datasets.
True or False: PCA requires the data to be normalized before applying the technique.
💡 Hint: Think about the first step in PCA.
2 more questions available
Challenge Problems
Push your limits with advanced challenges
You have a high-dimensional dataset containing features from several products. Describe how you would apply PCA to simplify your analysis and what insights you hope to gain.
💡 Hint: Think about the goals when simplifying a dataset.
Critique the use and limitation of PCA in machine learning, especially regarding its effectiveness with non-linear datasets.
💡 Hint: Consider how PCA aligns with the dataset’s inherent relationships.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.