Course curriculum

  • 01

    Foundations for Machine Learning Mini Bootcamp

  • 02

    Introduction, who am I and why this course?

    • Introduction, who am I and why this course?

  • 03

    Linear Algebra: Data Structures for Algebra

    • What Linear Algebra Is

    • What Linear Algebra Is - Example

    • A Brief History of Algebra

    • Exercise

    • Solution

    • Tensors & Scalars

    • Vectors and Vector Transposition

    • Norms & Unit Vectors

    • Basis, Orthogonal, and Orthonormal Vectors

    • Matrices and Tensors in TensorFlow and PyTorch

    • Exercise

    • Solution

  • 04

    Linear Algebra: Common Tensor Operations

    • Tensor Transposition, Arithmetic, Reduction & Dot Product

    • Exercise

    • Solution

    • Solving Linear Systems and Exercises

    • Solution

    • Solving Linear System with Elimination

    • Exercise

    • Solution

  • 05

    Linear Algebra : Matrix Properties

    • The Frobenius Norm

    • Matrix Multiplication

    • Symmetric and Identity Matrices

    • Exercise

    • Solution

    • Matrix Inversion

    • Diagonal Matrices

    • Orthogonal Matrices

  • 06

    Linear Algebra : Eigendecomposition

    • Linear Algebra Review and Applying Matrices in Exercise

    • Solution

    • Hands-on Code Demo

    • Eigenvectors

    • Eigenvalues

    • Hands-on Code Demo

    • Matrix Determinants with Hands-on Demo

    • Exercise

    • Solution and Explanation

    • Determinants and Eigenvalues

    • Eigendecomposition

    • Exercise

    • Applications of Eigendecomposition

  • 07

    Linear Algebra : Matrix Operations for Machine Learning

    • Singular Value Decomposition (SVD)

    • The Moore-Penrose Pseudoinverse

    • Hands-on Code Demo

    • Principal Component Analysis (PCA): A Simple ML Algorithm