Linear Algebra for Beginners: Open Doors to Great Careers
Richard Han, PhD in Math


1. Introduction Lecture
3:03 
2. Gaussian Elimination Systems of 2 Equations
11:13 
3. Gaussian Elimination and Row Echelon Form Systems of 3 Equations
18:15 
4. Elementary Row Operations
11:13 
5. Elementary Row Operations Additional Example
6:32 
6. Vector Operations and Linear Combinations
18:57 
7. Vector Equations and the Matrix Equation Ax=b
16:16 
8. Linear Independence
6:26 
9. Linear Independence Example 1
11:02 
10. Linear Independence Example 2
4:36 
11. Matrix Operations Addition and Scalar Multiplication Corrected (Am)
7:12 
12. Matrix Operations Multiplication
9:18 
13. Commutativity, Associativity, and Distributivity
13:13 
14. Identities, Additive Inverses, Multiplicative Associativity and Distributivity
14:25 
15. Transpose of a Matrix
6:42 
16. Inverse Matrix
5:30 
17. Gauss Jordan Elimination
10:56 
18. Gauss Jordan Elimination Additional Example
6:03 
19. Determinant of a 2 by 2 Matrix
2:34 
20. Cofactor Expansion
7:18 
21. Cofactor Expansion Additional Examples
5:51 
22. Determinant of a Product of Matrices and of a Scalar Multiple of a Matrix
11:07 
23. Determinants and Invertibility
7:26 
24. Determinants and Transposes
3:35 
25. Vector Space Definition
7:22 
26. Vector Space Example
13:43 
27. Vector Space Example Continued
12:18 
28. Vector Space Additional Example
16:46 
29. Vector Space Additional Example Continued
4:03 
30. Examples of Sets that are Not Vector Spaces
6:09 
31. Subspace Definition and Subspace Properties
9:55 
32. Definition of Trivial and Nontrivial Subspace
3:38 
33. Additional Example of Subspace
5:17 
34. Subsets that are Not Subspaces
9:13 
35. Subsets that are Not Subspaces Additional Example
4:25 
36. Span
15:26 
37. Span of a Subset of a Vector Space
8:24 
38. Linear Independence 2
9:35 
39. Determining Linear Independence or Dependence
13:43 
40. Basis
16:07 
41. Dimension
9:52 
42. Coordinates
3:27 
43. Change of Basis
9:23 
44. Examples of Finding Transition Matrices
13:02

About This Class
Would you like to learn a mathematics subject that is crucial for many highdemand lucrative career fields such as:
 Computer Science
 Data Science
 Actuarial Science
 Financial Mathematics
 Cryptography
 Engineering
 Computer Graphics
 Economics
If you're looking to gain a solid foundation in Linear Algebra, allowing you to study on your own schedule at a fraction of the cost it would take at a traditional university, to further your career goals, thisÂ onlineÂ courseÂ is for you. If you're a working professional needing aÂ refresherÂ on linear algebra or aÂ complete beginnerÂ who needs to learn Linear Algebra for the first time, this online course is for you.
Why you should take this online course: You need to refresh your knowledge of linear algebra for your career to earn a higher salary. You need to learn linear algebra because it is a required mathematical subject for your chosen career field such as computer science or electrical engineering. You intend to pursue a masters degree or PhD, and linear algebra is a required or recommended subject.
Why you should choose this instructor: I earned my PhD in Mathematics from the University of California, Riverside. I have extensive teaching experience: 6 years as a teaching assistant at University of California, Riverside, over two years as a faculty member at Western Governors University, #1 in secondary education by the National Council on Teacher Quality, and as a faculty member at Trident University International.
In this course, I cover the core concepts such as:
 Gaussian elimination
 Vectors
 Matrix Algebra
 Determinants
 Vector Spaces
 Subspaces
After taking this course, you will feelÂ CAREFREE AND CONFIDENT. I will break it all down into bitesized nobrainer chunks.Â I explain each definition and go through each exampleÂ STEP BY STEPÂ so that you understand each topic clearly.Â
Practice problemsÂ are provided for you, andÂ detailed solutionsÂ are also provided to check your understanding.
Grab a cup of coffee and start listening to the first lecture.Â Enroll now!