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Course Outline
Introduction to Linear Algebra
Why You Should Improve Your Linear Algebra Knowledge for Machine Learning
Learning Linear Algebra Notations
Understanding Vectors
- Vector Properties and Characteristics
- Performing Vector Operations
Understanding Matrices
- Matrix Properties and Characteristics
- Performing Matrix Operations and Transformations
- Working with Special Matrices
Solving Linear Systems
- Representing Problems as Linear Systems
- Solving Linear Systems
Linear Mappings with Matrices
- Orthogonal Matrices
- The Gram-Schmidt Process
Reflecting and Manipulating Images with Matrices
Understanding Eigenvalues and Eigenvectors and their Application to Data Problems
Examining Google's PageRank Algorithm with Eigenvalues and Eigenvectors
Understanding Principal Components Analysis (PCA) for Machine Learning
Understanding Linear Regression for Machine Learning
Project: Solving a Machine Learning Problem with Linear Algebra
Summary and Conclusion
Requirements
- Basic experience or familiarity with machine learning
- Basic programming experience
14 Hours