What you would learn in Machine Learning Foundations: Linear Algebra course?
Ever wondered what's going on beneath the machine learning algorithm? The answer lies in linear algebra. In the absence of it, machine learning wouldn't exist. Linear algebra is an essential component for understanding and constructing the majority of machine learning techniques, particularly ones that support neural networks and natural language processing tools, and deep learning models.
Take a seat with teacher Terezija Semenski to study the basic notions of linear algebra and the strategies needed to develop and create a successful machine learning algorithm. Learn the fundamentals of vector arithmetic. Also, I learned about vector norms as well as matrix properties, complex operations, and matrix transformation, and algorithms such as Google PageRank. When you've finished this class, you'll be able to apply the concepts part of linear algebra and use them in your next Machine Learning project.
- 1. Introduction to Linear Algebra
- 2. Vectors Basics
- 3. Vector Projections and Basis
- 4. Introduction to Matrices
- 5. Gaussian Elimination
- 6. Matrices from Orthogonality to Gram–Schmidt Process
- 7. Eigenvalues and Eigenvectors
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