Linear Algebra for Machine Learning and Data Science

Luis Serrano presents linear algebra through the calculations and geometric ideas used in machine learning. The four modules move from linear systems and elimination to vectors, transformations, determinants and eigenvectors. Python labs make this a practical bridge between school mathematics and data-science work, with programming experience expected rather than taught from scratch.

What you will learn

• Relate rank to the solutions of a linear system.
• Compose transformations through matrix multiplication.
• Connect eigenvectors with dimensionality reduction.

Who this suits

This intermediate course suits learners with school algebra and some Python. Review loops, functions, collections and debugging before beginning the labs.

Before you enroll

Mathematical understanding and coding fluency are separate demands. Budget time for both, and compare this with a general university linear-algebra course if you want broader theory.

Coursera lists paid enrollment or subscription access. Check the enrollment screen for included assessments, certificate terms, trials and any financial-aid options. University authorship does not by itself confer admission, academic credit or a professional license.

Put it into practice

Create a two-dimensional point cloud and apply a rotation and scaling in different orders. Explain why the final coordinates differ and show the corresponding matrix products.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

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