About Course

Dive deep into the world of Linear Algebra with our comprehensive Linear Algebra Full Course, offered on Today’s School. This course provides an in-depth exploration of key concepts such as vector spaces, linear transformations, eigenvalues, and matrix theory. Designed for both beginners and advanced learners, the curriculum includes:

  • Vector Spaces: Understand the principles of vector spaces, subspaces, and linear independence.
  • Matrices and Operations: Learn about matrix operations, inverses, determinants, and their applications.
  • Linear Transformations: Explore linear mappings, matrix representations, and the impact on vector spaces.
  • Eigenvalues and Eigenvectors: Gain insights into eigenvalues, eigenvectors, and their significance in solving systems of linear equations.
  • Applications and Problem Solving: Apply concepts to real-world problems and advanced topics in engineering, physics, and computer science.

Our course features interactive lectures, practical exercises, and assessments to ensure mastery of the material. Perfect for students aiming to excel in mathematics or related fields, this course equips you with the analytical skills needed for complex problem-solving and theoretical understanding. Enroll now and unlock the full potential of linear algebra with Today’s School!

Master Linear Algebra with our comprehensive course is now available at a special sale price! Dive into vector spaces, matrix theory, and more, with interactive lessons and practical exercises. Don’t miss out on this limited-time offer!

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What Will You Learn?

  • This course provides an in-depth exploration of key concepts such as vector spaces, linear transformations, eigenvalues, and matrix theory. Designed for both beginners and advanced learners.

Course Content

Linear Algebra

  • Vectors and Operations
    00:00
  • Systems of Linear Equations
    00:00
  • Determinant of a Matrix
    00:00
  • Matrix and its Classification
    00:00
  • Linear Transformation
    00:00
  • Eigenvalue and Eigenvector
    00:00
  • Matrix Algebra
    00:00
  • Eigenvalue and Eigenvector Decomposition
    00:00
  • Singular Value Decomposition
    00:00
  • Orthogonality
    00:00
  • Vector and Matrix Norms
    00:00
  • Inner Product Spaces and Vector Calculus
    00:00
  • Polynomials and Matrices
    00:00
  • Finite Fields Error Correcting Code
    00:00
  • Robotics and Control Theory
    00:00
  • Machine Learning and statistics
    00:00
  • Linear Programming
    00:00
  • Linear Algebra in Computer Graphics
    00:00
  • Operations Research
    00:00
  • Network Flow Problems
    00:00
  • Markov Chain
    00:00
  • Chaos Theory
    00:00
  • LU Decomposition
    00:00
  • Principle Component Analysis
    00:00
  • QR Decomposition
    00:00
  • Inverse Matrix
    00:00
  • Choleskey Decomposition
    00:00

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