Fundamentals to Linear Algebra

Name: Fundamentals to Linear Algebra
Price: 9 USD
Availability: InStock

20h 53m 19s

English

Paid

December 20, 2024

Unleash the power of linear algebra to conquer the world of data science, machine learning, and artificial intelligence. This intensive course will transform you into a master of mathematics, ready to tackle real-world challenges with practical skills and unwavering confidence.

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#	Title	Duration
1	Welcome Message	14:18
2	Linear Algebra RoadMap 2024	19:12
3	Pre-Requisites Introduction	09:03
4	Refreshment - Norms & Euclidean Distance	10:31
5	Refreshment - Real Numbers and Vector Space	04:14
6	Refreshment - Cartesian Coordinate System & Unit Circle	04:45
7	Refreshment - Angles, Unit Circle and Trigonometry	13:24
8	Refreshment - Pythagorean Theorem & Orthogonality	05:25
9	Why these Pre-Requisites Matter	02:28
10	Module 2.1: Foundations of Vectors	30:55
11	Module 2.2: Special Vectors and Operations	56:53
12	Module 2.3: Part 1 - Scalar Multiplication	25:06
13	Module 2.3 Part 2 - Linear Combination and Unit Vectors	47:10
14	Module 2.3 Part 3 - Span of Vectors	40:06
15	Module 2.3: Part 4 - Linear Independence	31:53
16	Module 2.4: Dot Product, Cauchy-Schwarz Inequality and Its	01:29:45
17	Module 1: Foundations of Linear Systems and Matrices	16:50
18	Module 2: Introduction to Matrices	30:01
19	Module 3: Core Matrix Operations	35:01
20	Module 4: Part 1 Solving Linear Systems - Gaussian Reduction	47:23
21	Module 4: Part 2 Solving Linear Systems - Gaussian Reduction	01:07:47
22	Module 4: Part 3 Solving Linear Systems - Gaussian Reduction	01:10:00
23	Module 4: Part 4 Solving Linear Systems - Gaussian Reduction	53:47
24	Module 1: Algebraic Laws for Matrices	56:31
25	Module 2: Determinants and Their Properties	50:52
26	Module 3: Matrix Inverses and Identity Matrix	01:03:32
27	Module 4: Transpose of Matrices: Properties and Applications	23:15
28	Module 1: Part 1 Basis of Vector Space	35:39
29	Module 1: Part 2 Vector Projection and Calculation	41:12
30	Module 1: Part 3 Gram-Schmidt Process	36:53
31	Module 2: Special Matrices and Their Properties	14:39
32	Module 3: Matrix Factorization, Examples and Applications	27:03
33	Module 4: QR Decomposition Overview	42:52
34	Module 5: Eigenvalues, Eigenvectors, and Eigen Decomposition	01:16:31
35	Module 6: Singular Value Decomposition (SVD)	58:23