Linear Algebra for Machine Learning
5.0
(1)
17 learners
What you'll learn
This course includes
- 6.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 48 lessons • 6.5 hours of video
Linear Algebra for Machine Learning
48 lessons
• 6.5 hours
Linear Algebra for Machine Learning
48 lessons
• 6.5 hours
- Machine Learning Foundations: Welcome to the Journey02:38
- What Linear Algebra Is — Topic 1 of Machine Learning Foundations24:04
- Plotting a System of Linear Equations — Machine Learning Foundations Bonus Video09:19
- Linear Algebra Exercise — Topic 2 of Machine Learning Foundations02:05
- Tensors — Topic 3 of Machine Learning Foundations02:34
- Scalars — Topic 4 of Machine Learning Foundations13:05
- Vectors and Vector Transposition — Topic 5 of Machine Learning Foundations12:19
- Norms and Unit Vectors — Topic 6 of Machine Learning Foundations15:10
- Basis, Orthogonal, and Orthonormal Vectors — Topic 7 of Machine Learning Foundations04:30
- Matrix Tensors — Topic 8 of Machine Learning Foundations08:24
- Generic Tensor Notation — Topic 9 of Machine Learning Foundations06:44
- Exercises on Algebra Data Structures — Topic 10 of Machine Learning Foundations00:42
- Tensor Operations — Segment 2 of Subject 1, "Intro to Linear Algebra", ML Foundations01:20
- Tensor Transposition — Topic 11 of Machine Learning Foundations03:53
- Basic Tensor Arithmetic (The Hadamard Product) — Topic 12 of Machine Learning Foundations06:13
- Tensor Reduction — Topic 13 of Machine Learning Foundations03:32
- The Dot Product — Topic 14 of Machine Learning Foundations05:14
- Exercises on Tensor Operations — Topic 15 of Machine Learning Foundations00:57
- Solving Linear Systems with Substitution — Topic 16 of Machine Learning Foundations04:04
- Solving Linear Systems with Elimination — Topic 17 of Machine Learning Foundations05:52
- Visualizing Linear Systems — Machine Learning Foundations Bonus Video10:59
- Matrix Properties — Final Segment of Subject 1, "Intro to Linear Algebra", ML Foundations02:06
- The Frobenius Norm — Topic 18 of Machine Learning Foundations05:02
- Matrix Multiplication — Topic 19 of Machine Learning Foundations25:00
- Symmetric and Identity Matrices — Topic 20 of Machine Learning Foundations04:42
- Matrix Multiplication Exercises — Topic 21 of Machine Learning Foundations00:52
- Matrix Inversion — Topic 22 of Machine Learning Foundations17:07
- Diagonal Matrices — Topic 23 of Machine Learning Foundations03:26
- Orthogonal Matrices — Topic 24 of Machine Learning Foundations05:50
- Orthogonal Matrix Exercises — Topic 25 of Machine Learning Foundations02:11
- Linear Algebra II: Matrix Operations — Subject 2 of Machine Learning Foundations17:53
- Applying Matrices — Topic 26 of Machine Learning Foundations07:32
- Affine Transformations — Topic 27 of Machine Learning Foundations18:53
- Eigenvectors and Eigenvalues — Topic 28 of Machine Learning Foundations26:47
- Matrix Determinants — Topic 29 of Machine Learning Foundations08:05
- Determinants of Larger Matrices — Topic 30 of Machine Learning Foundations08:42
- Determinant Exercises — Topic 31 of Machine Learning Foundations01:28
- Determinants and Eigenvalues — Topic 32 of Machine Learning Foundations16:16
- Eigendecomposition — Topic 33 of Machine Learning Foundations12:49
- Eigenvector and Eigenvalue Applications — Topic 34 of Machine Learning Foundations13:02
- Matrix Operations for Machine Learning — Final Segment of Subject 2, "Linear Algebra II"03:22
- Singular Value Decomposition — Topic 35 of Machine Learning Foundations10:50
- Data Compression with SVD — Topic 36 of Machine Learning Foundations11:33
- The Moore-Penrose Pseudoinverse — Topic 37 of Machine Learning Foundations12:23
- Regression with the Pseudoinverse — Topic 38 of Machine Learning Foundations18:57
- The Trace Operator — Topic 39 of Machine Learning Foundations04:37
- Principal Component Analysis (PCA) — Topic 40 of Machine Learning Foundations08:27
- Linear Algebra Resources — Topic 41 of Machine Learning Foundations06:11
