Linear Algebra for Machine Learning and Data Science

This course introduces the core linear algebra tools that power machine learning and data science. You will learn to represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence. The syllabus covers systems of linear equations, vector operations, linear transformations, determinants, and eigenvectors — each topic built to solve real machine‑learning problems.

What sets this course apart is its focus on application rather than theory alone. You will apply common vector and matrix algebra operations — dot product, inverse, and determinants — and express certain matrix operations as linear transformations. The course then shows how eigenvalues and eigenvectors are used in machine‑learning contexts such as dimensionality reduction and image analysis.

By the end you can solve systems of linear equations, compute matrix inverses and determinants, and interpret eigenvectors for data‑reduction tasks. You will also be able to write basic Python code using NumPy to perform these operations. The course assumes high‑school math (functions, basic algebra) and some programming experience with loops and functions, making it accessible to beginners who meet the prerequisites.

Honest limits: the course does not cover advanced calculus, probability, or deep‑learning architecture design. Learners who need a stronger foundation in calculus should look at the companion course Calculus for Machine Learning and Data Science within the same specialization. Those seeking hands‑on projects that build complete ML pipelines may want to pair this with a programming‑focused course such as the Python for Data Science and Machine Learning Bootcamp on Udemy, which offers project‑based practice alongside its video lessons.