Almost everything in AI is stored and processed as numbers arranged in grids. A single data point such as a house, an image, or a sentence becomes a vector, and a whole dataset or a neural network layer becomes a matrix. This module shows you how to work with both.
The lessons start with a beginner-friendly overview of vectors and matrices in AI, then cover the two operations you'll use constantly: the dot product (a way to measure how similar two vectors are) and matrix multiplication (many dot products at once, and the core of every neural network layer). They finish with hands-on coding walkthroughs using NumPy.
Watch the lessons in order, then continue on to Eigenvalues & Decompositions.
A beginner-friendly lesson on what vectors and matrices are and why they are fundamental structures in artificial intelligence.
Video 8minA tutorial on the foundational maths of AI: how vectors and matrices interact through dot products and matrix multiplication.
Video 5minA focused look at the matrix-vector dot product, the operation at the heart of machine learning, neural networks, and data science.
Video 17minA beginner-friendly tutorial on doing linear algebra in Python with NumPy, so you can put vectors and matrices into code.
Video 10minA longer walkthrough that codes the maths from scratch and with NumPy: vectors, norms, matrix operations, dot product, transpose, determinant, and inverse.
Video 36min