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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⭐️ 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
Thank you for making this video. It's so helpful!
Great video for beginners
Thank you so much! This video is GOLD.
Thank you!I learned a lot from this video.
What happened to your fucking voice?Spoilt the lecture.
This classifies as ASMR
My college professor complete this lec in 2 hour at 1000 × and we can't understand anything that time now we are here .
now i dont regret missing those classes😂🤣
1:40 theorem 4
7:39:46 I think there is a mistake here. It should be 1-λ not -1-λ
You lady are awesome!
This is so good for College students ❤️
❤️❤️❤️❤️❤️
God bless the professor and God bless the channel! <3
Is it recommended to complete courses like that in one day or is it better to break them into small segments and tackle them bit by bit every day?
Applied linear algebra ka tutor bataiye sir exam ke lia,paid bhi chalega,carry over dena hai.
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.
Can we get Closed Captions somehow ?
this is what someone who lost the will to live sounds like lmao
great work girl u this channels deserves all the subs
4:26 the title said its beginner to experts someone help me please what are she talking about ?
I though this is for beginners ?
very good video is helping a lot
Thank you so much may god bless you as you blessed me with this knowledge
5:30:30 is phenomenally well explained, the use of colour to differentiate the null space and column space is the best thing I could've ask for.
Thank you, amazing human
i from 2032 but still watching it
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
Does this course contain the basics of sets, relations, recurrences, simple combinatorial problems, Matrices and basic matrix algebra? Someone, please answer.
it is really helpful 👍😁
I hate English I love hindi
46:45 I do not understand why is it always consistent. Can someone explain it better? Thanks!
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?
Kisan mazdoor ekta zindabad!!🚜
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