Dimensionality Reduction Essentials

About this course

Master the essential building blocks of dimensionality reduction.

• 3 lessons
• 15 min
• beginner

What you'll learn

• Explain the curse of dimensionality and its impact on model generalizability
• Identify the relationship between feature space sparsity and model performance degradation
• Distinguish between feature selection and feature extraction approaches
• Recognize when dimensionality reduction is necessary in a machine learning workflow
• Explain how Principal Component Analysis maximizes variance in lower-dimensional representations
• Describe the role of eigenvectors and eigenvalues in PCA computation

Finishing the course

• 12 knowledge-check questions along the way, so you know the ideas stuck before you move on.
• Watching is free and needs no account. Sign in to save your progress. Pass each lesson's quiz (60%+) to earn the certificate. Each certificate has its own public verification link to share, marked quiz-verified.
• Part of the Machine Learning Fundamentals and Applications learning path. Enrol in the path and finish its required courses to earn one certificate for all of them.

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