This course provides the mathematical foundation required for machine learning and artificial intelligence. It introduces the core components of linear algebra; vectors, matrices, and their operations; from a practical standpoint. You will learn how these mathematical objects are used to represent data and perform transformations, which are fundamental operations in many machine learning algorithms. The material is presented with a focus on intuition and application, using Python and the NumPy library to translate theory into practice. By the end, you will have a solid understanding of the linear algebra concepts that underpin modern AI.

Prerequisites Basic Python helpful

Level:

Beginner

Represent Data with Vectors and Matrices
Understand how to represent data points, features, and entire datasets using vectors and matrices.
Perform Core Linear Algebra Operations
Execute fundamental operations such as vector addition, dot products, matrix multiplication, and transposition.
Interpret Geometric Transformations
Visualize how matrices act as functions to rotate, scale, and shear vector spaces.
Solve Systems of Linear Equations
Set up and solve systems of linear equations, a common task in optimization and model fitting.
Understand Eigenvalues and Eigenvectors
Grasp the concepts of eigenvalues and eigenvectors and their significance in algorithms like Principal Component Analysis (PCA).
Implement Concepts in Python
Use the NumPy library in Python to perform linear algebra computations efficiently.

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