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Johns Hopkins University

Linear Algebra from Elementary to Advanced

Johns Hopkins University via Coursera Specialization


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This specialization is a three course sequence that will cover the main topics of undergraduate linear algebra. Defined simply, linear algebra is a branch of mathematics that studies vectors, matrices, lines and the areas and spaces they create. These concepts are foundational to almost every industry and discipline, giving linear algebra the informal name "The Theory of Everything". This specialization assumes no prior knowledge of linear algebra and requires no calculus or similar courses as a prerequisite. The first course starts with the study of linear equations and matrices. Matrices and their properties, such as the determinant and eigenvalues are covered. The specialization ends with the theory of symmetric matrices and quadratic forms. Theory, applications, and examples are presented throughout the course. Examples and pictures are provided in low dimensions before abstracting to higher dimensions. An equal emphasis is placed on both algebraic manipulation as well as geometric understanding of the concepts of linear algebra. Upon completion of this specialization , students will be prepared for advanced topics in data science, AI, machine learning, finance, mathematics, computer science, or economics.


Course 1: Linear Algebra: Linear Systems and Matrix Equations
- Offered by Johns Hopkins University. This is the first course of a three course specialization that introduces the students to the concepts ... Enroll for free.

Course 2: Linear Algebra: Matrix Algebra, Determinants, & Eigenvectors
- Offered by Johns Hopkins University. This course is the second course in the Linear Algebra Specialization. In this course, we continue to ... Enroll for free.

Course 3: Linear Algebra: Orthogonality and Diagonalization
- Offered by Johns Hopkins University. This is the third and final course in the Linear Algebra Specialization that focuses on the theory and ... Enroll for free.


Taught by

Joseph W. Cutrone, PhD


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