Linear Algebra Fundamentals
Discover the power of linear algebra and its applications in computer science, data science, machine learning, and engineering. This course provides a solid foundation in vector spaces, matrices, and linear transformations.
What You'll Learn
- Vector Spaces and Subspaces
- Matrix Operations and Properties
- Linear Transformations
- Eigenvalues and Eigenvectors
- Orthogonality and Least Squares
- Applications in Machine Learning
Course Description
Linear algebra is the branch of mathematics concerning linear equations, linear transformations, and their representations in vector spaces and through matrices. It is a fundamental subject with applications across all areas of mathematics, science, and engineering.
This comprehensive course covers all essential topics in linear algebra, from basic vector operations to advanced concepts like eigenvalue decomposition and singular value decomposition. You'll learn not just the theory, but also how these concepts are applied in real-world scenarios, particularly in data science, machine learning, and computer graphics.
Each lesson includes visual explanations to help you develop geometric intuition, computational exercises to build your problem-solving skills, and real-world examples showing how linear algebra is used in modern technology. You'll work with practical applications including image compression, Google's PageRank algorithm, and neural network fundamentals.
Whether you're a student preparing for exams, a data scientist looking to strengthen your mathematical foundation, or a professional working with machine learning algorithms, this course will give you the solid understanding of linear algebra you need to succeed.