Crunching Numbers

Linear algebra is essential in data science and machine learning. Huge datasets are often stored as matrices, and algorithms use linear algebra to find patterns.

• Predicting Outcomes: Systems of equations help make predictions based on data.
• Principal Component Analysis: This technique uses eigenvalues and eigenvectors to reduce data complexity.

Why It Matters

Every time you get a movie recommendation or see a face recognized in photos, linear algebra is at work behind the scenes.

Try It

• Represent a dataset as a matrix and perform basic operations.
• Use eigenvalues to reduce a dataset's dimensions.