Linear Algebra Full Course for Beginners to Experts

Linear Algebra Full Course for Inexperienced persons to Specialists

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Linear algebra is central to nearly all areas of arithmetic. For example, linear algebra is key in trendy displays of geometry, together with for outlining fundamental objects reminiscent of strains, planes and rotations. Additionally, useful evaluation could also be mainly considered as the applying of linear algebra to areas of features. Linear algebra can also be utilized in most sciences and engineering areas, as a result of it permits modeling many pure phenomena, and effectively computing with such fashions.

⭐️ Desk of Contents ⭐
(0:00) Linear Algebra – Methods of Linear Equations (1 of three)
(16:20) Linear Algebra – System of Linear Equations (2 of three)
(27:55) Linear Algebra – Methods of Linear Equations (Three of three)
(47:18) Linear Algebra – Row Discount and Echelon Types (1 of two)
(54:49) Linear Algebra – Row Discount and Echelon Types (2 of two)
(1:4:10) Linear Algebra – Vector Equations (1 of two)
(1:14:05) Linear Algebra – Vector Equations (2 of two)
(1:24:54) Linear Algebra – The Matrix Equation Ax = b (1 of two)
(1:39:21) Linear Algebra – The Matrix Equation Ax = b (2 of two)
(1:44:48) Linear Algebra – Answer Units of Linear Methods
(1:57:49) Linear Algebra – Linear Independence
(2:11:20) Linear Algebra – Linear Transformations (1 of two)
(2:25:10) Linear Algebra – Linear Transformations (2 of two)
(2:39:19) Linear Algebra – Matrix Operations
(2:56:24) Linear Algebra – Matrix Inverse
(3:12:17) Linear Algebra – Invertible Matrix Properties
(3:24:24) Linear Algebra – Determinants (1 of two)
(3:44:40) Linear Algebra – Determinants (2 of two)
(4:04:28) Linear Algebra – Cramer’s Rule
(4:18:20) Linear Algebra – Vector Areas and Subspaces (1 of two)
(4:48:30) Linear Algebra – Vector Areas and Subspaces
(5:13:13) Linear Algebra – Null Areas, Column Areas, and Linear Transformations
(5:33:25) Linear Algebra – Foundation of a Vector Area
(5:59:43) Linear Algebra – Coordinate Methods in a Vector Area
(6:15:41) Linear Algebra – Dimension of a Vector Area
(6:26:35) Linear Algebra – Rank of a Matrix
(6:50:09) Linear Algebra – Markov Chains
(7:09:23) Linear Algebra – Eigenvalues and Eigenvectors
(7:32:03) Linear Algebra – Matrix Diagonalization
(7:49:08) Linear Algebra – Interior Product, Vector Size, Orthogonality

⭐️ Credit score ⭐️
Developed by Dr. Betty Love on the College of Nebraska – Omaha to be used in MATH 2050, Utilized Linear Algebra.

Based mostly on the guide Linear Algebra and Its Functions by Lay

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35 thoughts on “Linear Algebra Full Course for Inexperienced persons to Specialists”

  1. ⭐️ Learn more: https://youtube.com/playlist?list=PLKp3X-578hN8s5htUiN-8yO38BR4eaPsb
    ⭐️ Table of Contents ⭐
    (0:00) Linear Algebra – Systems of Linear Equations (1 of 3)

    (16:20) Linear Algebra – System of Linear Equations (2 of 3)

    (27:55) Linear Algebra – Systems of Linear Equations (3 of 3)

    (47:18) Linear Algebra – Row Reduction and Echelon Forms (1 of 2)

    (54:49) Linear Algebra – Row Reduction and Echelon Forms (2 of 2)

    (1:4:10) Linear Algebra – Vector Equations (1 of 2)

    (1:14:05) Linear Algebra – Vector Equations (2 of 2)

    (1:24:54) Linear Algebra – The Matrix Equation Ax = b (1 of 2)

    (1:39:21) Linear Algebra – The Matrix Equation Ax = b (2 of 2)

    (1:44:48) Linear Algebra – Solution Sets of Linear Systems

    (1:57:49) Linear Algebra – Linear Independence

    (2:11:20) Linear Algebra – Linear Transformations (1 of 2)

    (2:25:10) Linear Algebra – Linear Transformations (2 of 2)

    (2:39:19) Linear Algebra – Matrix Operations

    (2:56:24) Linear Algebra – Matrix Inverse

    (3:12:17) Linear Algebra – Invertible Matrix Properties

    (3:24:24) Linear Algebra – Determinants (1 of 2)

    (3:44:40) Linear Algebra – Determinants (2 of 2)

    (4:04:28) Linear Algebra – Cramer's Rule

    (4:18:20) Linear Algebra – Vector Spaces and Subspaces (1 of 2)

    (4:48:30) Linear Algebra – Vector Spaces and Subspaces

    (5:13:13) Linear Algebra – Null Spaces, Column Spaces, and Linear Transformations

    (5:33:25) Linear Algebra – Basis of a Vector Space

    (5:59:43) Linear Algebra – Coordinate Systems in a Vector Space

    (6:15:41) Linear Algebra – Dimension of a Vector Space

    (6:26:35) Linear Algebra – Rank of a Matrix

    (6:50:09) Linear Algebra – Markov Chains

    (7:09:23) Linear Algebra – Eigenvalues and Eigenvectors

    (7:32:03) Linear Algebra – Matrix Diagonalization

    (7:49:08) Linear Algebra – Inner Product, Vector Length, Orthogonality

    Reply

  18. I had a terrible instructor for this class. I still got an A, but after watching parts of this video I've learning the pieces I was missing. Also this video is spot on for all the class encompassed, although I didn't see isomorphism.

    Reply

  29. You have made my Linear Algebra course feel like a breeze with your method of teaching, especially knowing my current professor, despite knowing the content, is extremely bad at explaining. I really appreciate your hard work, thank you! The use of colour is extremely efficient at learning, which I'm glad you incorporated, especially at 3:29:00

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  34. At 2:03:40, how can it be linearly dependent for having no free variable, while having none of the vectors in the set be a multiple of another within that set? Or is that strictly for when there are only two vectors in a set?

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