Linear Algebra for Data Science: Practical Applications with Python

Overview

Learn to apply essential linear algebra techniques including singular value decomposition (SVD) and principal component analysis (PCA) for advanced data manipulation, feature engineering, and dimensionality reduction in data science projects using Python. Master Pythonic linear algebra implementations to develop practical applications and solve real-world problems across various domains. Gain hands-on experience with 20 hours of comprehensive instruction covering the mathematical foundations and practical programming skills needed to leverage linear algebra effectively in data science workflows.

Syllabus

Use linear algebra techniques like singular value decomposition (SVD) and principal component analysis (PCA), for advanced data manipulation, feature engineering, and dimensionality reduction in data science projects using Python.
Apply Pythonic linear algebra to develop practical applications or solve real-world problems in their domains