Linear Algebra Fundamentals
Dive deep into linear algebra, the mathematical foundation of modern data science, machine learning, and computer graphics. This comprehensive course covers vector spaces, linear transformations, matrices, determinants, eigenvalues, and eigenvectors.
Ideal for students in computer science, data science, engineering, and anyone looking to build a strong foundation in linear algebra for practical applications.
What You'll Learn:
- Vector spaces and subspaces theory
- Matrix operations and properties
- Linear transformations and mappings
- Eigenvalues and eigenvectors applications
- Orthogonality and least squares
- Applications in machine learning and data science
Course Curriculum
Module 1: Vectors and Vector Spaces
Introduction to vectors, vector operations, linear combinations, and vector spaces
Module 2: Matrices and Systems
Matrix algebra, solving systems of linear equations, matrix inverses
Module 3: Linear Transformations
Understanding linear transformations, kernel, range, and matrix representations
Module 4: Eigenvalues and Eigenvectors
Computing eigenvalues, diagonalization, and applications
Module 5: Applications
Real-world applications in data science, machine learning, and computer graphics
Meet Your Instructor
Prof. Sarah Martinez
Ph.D. in Applied Mathematics with specialization in linear algebra and its applications. Prof. Martinez has 12 years of experience teaching at top universities and has published numerous papers on linear algebra applications in machine learning.
Student Reviews
"Perfect course for anyone getting into data science. The connections to real-world applications are invaluable."
"Clear explanations and great examples. This course helped me understand the math behind machine learning algorithms."