MIT · on MIT OpenCourseWare
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.
Beginners building toward ML. This is an intermediate course in Data Science & Math for AI, running about 35 lectures plus problem sets and exams.
Free. No payment required at any point.
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.
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.