The prerequisites, taught well. Whether you need to brush up on matrices or learn Python and pandas from zero, these courses close the gap so the ML courses make sense.
Sorted cheapest first. Click a column header to re-sort. Beginners building toward ML.
| Course | Level | Duration | Price | Certificate | |
|---|---|---|---|---|---|
Khan Academy |
beginner | Self-paced (3 units: vectors and spaces, matrix transformations, alternate coordinate systems) | Free | No | Visit |
MIT · MIT OpenCourseWare |
intermediate | 35 lectures plus problem sets and exams | Free | No | Visit |
StatQuest (Josh Starmer, PhD) · YouTube |
beginner | Hundreds of 5-25 minute videos, self-paced | Free | No | Visit |
Google · Coursera |
beginner | 6 months at 10 hrs/week (9 courses, 180+ hours) | Free to audit Free to audit · $49/mo |
Yes | Visit |
IBM · Coursera |
beginner | 4 months at 10 hrs/week (12 courses, ~178 hours) | Free to audit Free to audit · $49/mo |
Yes | Visit |
DeepLearning.AI · Coursera |
beginner | 12 weeks at 5 hrs/week (3 courses, ~94 hours) | Free to audit Free to audit · $49/mo |
Yes | Visit |
Imperial College London · Coursera |
beginner | 3-4 months at 3-4 hrs/week (3 courses, ~58 hours) | Free to audit Free to audit · $49/mo |
Yes | Visit |
University of Michigan · Coursera |
beginner | 2 months at 10 hrs/week (5 courses, ~79 hours) | Free to audit Free to audit · $49/mo |
Yes | Visit |
Harvard University (HarvardX) · edX |
beginner | 1 year 5 months at 2-3 hrs/week (9 courses) | Free to audit Free to audit · $1,481 |
Yes | Visit |
No payment required. Several of these are complete university courses.
Sal Khan's patient video walkthroughs of vectors, span, null and column spaces, linear transformations, inverses, determinants, orthogonality, and eigenvectors. The place to start if you need linear algebra from scratch before any ML math course.
Gilbert Strang's legendary lectures on the four fundamental subspaces, projections, determinants, eigenvalues, and the SVD, with solved problem sets and exams. The gold standard for building the linear algebra intuition that underlies all of ML.
Clearly explained, song-punctuated videos on statistics fundamentals, regression, cross-validation, trees and boosting, PCA, and neural networks through transformers. The best quick-reference when a concept in another course does not click.
Watch everything free; pay only if you want graded assignments and a credential.
Google's entry-level analytics program covers the data lifecycle, spreadsheets, SQL, R, Tableau, and a case-study capstone. The best-known on-ramp for people with no technical background who want data skills before tackling ML.
Twelve courses from Python and SQL basics through data visualization, machine learning, and a capstone, with no prerequisites. Suited to career changers who want an employer-recognized, end-to-end data science credential.
Luis Serrano's visually intuitive treatment of linear algebra, calculus, and probability and statistics, with Python labs that tie each idea to a real ML algorithm. The friendliest full math foundation for ML beginners.
Three courses on linear algebra, multivariate calculus, and PCA, taught with an eye toward how each concept appears in ML. The classic math refresher for learners who found an ML course's notation intimidating.
Charles Severance's beloved introduction to programming in Python, databases, web data, and a capstone, assuming zero prior experience. The right first step for anyone who needs to learn to code before they can learn ML.
Rafael Irizarry's nine-course sequence teaches R, probability, inference, regression, machine learning, and productivity tools, ending in a capstone. For learners who prefer a statistics-first, university-branded path into data science.
Supervised and unsupervised learning, model evaluation, and the math behind it.
Understand what AI is, how it works, and how to use it — no coding required.