Linear Algebra from Elementary to Advanced

14.343 students enrolled
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.
CERTIFICATEKatılım Sertifikası
FORMAT100% Online
DURATION1 ay

What you'll learn

  • Linear Algebra
  • Geometry
  • Advanced Mathematics
  • Engineering Analysis
  • Applied Mathematics
  • Algebra
  • Mathematical Theory & Analysis
  • Markov Model
  • Applied Machine Learning
  • Artificial Intelligence and Machine Learning (AIu002FML)

Course Content

3 topics
  1. Kurs 1 Linear Algebra: Linear Systems and Matrix Equations
  2. Kurs 2 Linear Algebra: Matrix Algebra, Determinants, & Eigenvectors
  3. Kurs 3 Linear Algebra: Orthogonality and Diagonalization

Details

  • ProviderJohns Hopkins University
  • TypeCourse
  • CategoryData Science & AI
  • LevelBeginner
  • Duration1 ay
  • LanguageEnglish

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Melis Gülsar
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