Course curriculum

    1. Course Introduction

    2. Why Learn Linear Algebra?

    3. Linear Algebra Defined

    4. Numerical Linear Algebra

    5. Linear Algebra and Statistics

    1. NumPy Array Defined

    2. Functions to Create Arrays

    3. Combining Arrays

    4. From Lists to Arrays

    5. Indexing Arrays

    6. Slicing Arrays (Important Lesson)

    7. Reshaping Arrays

    8. Array Broadcasting

    9. Demo: Array Broadcasting

    10. Demo: Array Broadcasting Limitations

    1. Vectors

    2. Demo: Vector Arithmetic

    3. Demo: Vector Dot Product

    4. Matrices

    5. Defining, Adding and Subtracting a Matrix

    6. Matrix Multiplication and Division

    7. Matrix-Matrix Multiplication (Dot Product)

    8. Matrix Scalar Multiplication

    9. Matrix Operations: Transpose

    10. Matrix Operations: Inversion

    11. Matrix Operations: Trace and Determinant

    12. Matrix Operations: Rank

    13. Matrix Types: Square and Symmetric

    14. Matrix Types: Triangular and Diagonal

    15. Matrix Types: Identity and Orthogonal Matrix

    16. Spare Matrix

    17. Problems with Sparsity

    18. Sparse Matrices in Machine Learning

    19. Matrix Sparsity in Python

    20. Tensors

    21. Tensors in Python

    22. Tensor Dot Product

    1. Matrix Decomposition

    2. Matrix Decomposition: LU

    3. Matrix Decomposition: QR and Cholesky

    4. Eigenvectors and Eigenvalues

    5. Calculation of Eigen Decomposition

    6. Confirm and Reconstruct Eigenvector

    7. Singular-Value Decomposition

    8. Reconstruct Matrix from SVD

    9. SVD for Dimensionality Reduction

About this course

  • Free
  • 46 lessons
  • 1.5 hours of video content