« I’ve completed the entire Linear Algebra for Machine Learning playlist by Jon Krohn. Should I explore additional playlists to deepen my understanding of linear algebra for ML, or is it better to move on to the next major area of mathematics for machine learning, such as calculus or probability?

If yes, what should I start with next? (However, I haven’t started anything beyond this yet.) »

Also, Linear Algebra for Machine Learning by Jon Krohn playlist, covers the following topics:

SUBJECT 1 : INTRO TO LINEAR ALGEBRA (3 segments)

Segment 1: Data Structures for Algebra (V1- V11)

What Linear Algebra Is A Brief History of Algebra Tensors Scalars Vectors and Vector Transposition Norms and Unit Vectors Basis, Orthogonal, and Orthonormal Vectors Generic Tensor Notation Arrays in NumPy Matrices Tensors in TensorFlow and PyTorch

Segment 2: Common Tensor Operations (V12- V22)

Tensor Transposition Basic Tensor Arithmetic(Hadamard Product) Reduction The Dot Product Solving Linear Systems

Segment 3: Matrix Properties(V23-V30)

The Frobenius Norm Matrix Multiplication Symmetric and Identity Matrices Matrix Inversion Diagonal Matrices Orthogonal Matrices

SUBJECT 2 : Linear Algebra II: Matrix Operations (3 segments)

Segment 1:Review of Introductory Linear Algebra

Modern Linear Algebra Applications Tensors, Vectors, and Norms Matrix Multiplication Matrix Inversion Identity, Diagonal and Orthogonal Matrices

Segment 2: Eigendecomposition

Affine Transformation via Matrix Application Eigenvectors and Eigenvalues Matrix Determinants Matrix Decomposition Applications of Eigendecomposition

Segment 3: Matrix Operations for Machine Learning

Singular Value Decomposition (SVD) The Moore-Penrose Pseudoinverse The Trace Operator Principal Component Analysis (PCA): A Simple Machine Learning Algorithm Resources for Further Study of Linear Algebra