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This complete Linear Algebra crash course takes you from the basics to advanced concepts — step-by-step — with practical applications in Data Science, Machine Learning, and AI. Perfect for beginners and a great refresher for experienced learners.
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Timestamps:
1. 00:00:00 - Intro to Linear Algebra for Machine Learning
2. 00:14:00 - Notations in Linear Algebra
3. 00:21:44 - Sets, Norms, Vector Space
4. 00:29:25 - Addition, Subtraction & Trace of Matrix
5. 00:40:29 - Matrix Multiplication
6. 00:51:07 - Diagonal, Identity and Orthogonal Matrix
7. 01:03:08 - Singular, Non-Singular, Determinant
8. 01:12:12 - Inverse Matrix
9. 01:17:10 - Sparse & Dense Matrix
10. 01:27:55 - Rank of Matrix
11. 01:31:54 - Alternative to Matrix Division
12. 01:37:37- Symmetric Matrix
13. 01:43:03 - Variance, Correlation, Covariance
14. 01:55:52 - Covariance Matrix
15. 02:05:31 - Logarithm Properties
16. 02:15:34- Positive, Negative Definite Matrix
17. 02:21:42 - Convex & Non-Convex Functions
18. 02:31:27- Jensen's Inequality
19. 02:46:08 - Hessian Matrix
20. 03:01:49 - System of Linear Equations
21. 03:09:32 - Cramer's Rule
22. 03:19:21 - Gaussian Elimination
23. 03:33:06 - MCQ Exam - Link: https://forms.gle/LwXoiPTaJsNWgmSaA
24. 03:36:00 - Software Engineering with Machine Learning Roadmap

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