You can study machine learning for free online through textbooks, lecture notes, and course packages. The best starting point depends on your background and what kind of support you want: a book for sustained reading, notes for a particular course, or videos and exercises for guided self-study.
Free machine learning resources at a glance
| Resource | Best fit | Format and access described by the source |
|---|---|---|
| Introduction to Statistical Learning with Applications in Python (2023) | Readers looking for a broad textbook introduction | Tufts lists it among textbooks available free online, in-browser, or as downloadable PDFs. Tufts graduate student resources |
| LMU Munich Introduction to Machine Learning (I2ML) | Self-learners who want a course structure, with materials at undergraduate and MSc levels | Free introductory supervised-learning course with videos, PDF slides, cheatsheets, quizzes, exercises with solutions, and notebooks. I2ML course site |
| MIT OCW Machine Learning (6.867, Fall 2006) | Readers seeking graduate-level lecture notes | Archived course page with individual lecture PDFs. MIT OpenCourseWare course page |
| MIT OCW Algorithmic Aspects of Machine Learning (18.409, Spring 2015) | Readers interested in algorithmic and theoretical aspects | Graduate course materials, including lecture notes and textbook resources. MIT OpenCourseWare course page |
| University of Washington CSE 446 textbook references (Spring 2026) | Readers comparing textbook references, including a gentler introductory option | Course reference page points to a free PDF preprint of Kevin Murphy’s Probabilistic Machine Learning: An Introduction (2022) and identifies Hal Daumé III’s A Course in Machine Learning as a free online, gentler introduction. UW CSE 446 course page |
| Seoul National University Introduction to Machine Learning | Readers comfortable following scheduled readings and notes without a required textbook | Course schedule links readings and notes; there is no required textbook. SNU course page |
These are different kinds of resources, not a universal ranking. A course page can point to useful texts without establishing that every linked edition or copy has the same access terms.
Free online machine learning textbooks
Start with a broad introduction
Tufts’ Fall 2025 graduate student resources page lists Introduction to Statistical Learning with Applications in Python (2023) by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor among textbooks available free online, in-browser, or as downloadable PDFs. That access description makes it a practical option if you prefer working through a book rather than following a course schedule.
The same Tufts list includes Machine Learning – A First Course for Engineers and Scientists by Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, and Thomas B. Schön (2022); Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (MIT Press, 2016); and The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (second edition, 2009; corrected 12th printing, 2017). The list is a set of references, not a current-edition comparison or a guarantee that every version has identical access conditions.
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Try a gentler online introduction
The University of Washington’s Spring 2026 CSE 446 reference page describes Hal Daumé III’s A Course in Machine Learning as a free online, gentler introduction. It also names Kevin Murphy’s Probabilistic Machine Learning: An Introduction (2022) and points to a free PDF preprint. Use the page as a course reference list: it identifies resources, but does not establish open-licensed status for every edition or copy.
Free machine learning lecture notes and course materials
For guided self-study with exercises: LMU Munich I2ML
LMU Munich’s Introduction to Machine Learning calls itself an open and free introductory course on supervised machine learning. Its self-study materials include lecture videos, PDF slides, cheatsheets, quizzes, exercises with solutions, and notebooks. The site separates introductory undergraduate material from more advanced MSc-level sections, so start with the section that matches your preparation rather than assuming all content is equally introductory.
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For graduate lecture notes: MIT OpenCourseWare
MIT’s Machine Learning (6.867) page is for Fall 2006. It identifies lecture notes as a learning resource and links individual lecture PDFs. It can provide a structured set of notes, but its date matters: this is an archived course, not a recently updated curriculum.
MIT’s Algorithmic Aspects of Machine Learning (18.409) page is for Spring 2015. It lists lecture notes and other course materials, including textbook resources. Its graduate-level, algorithmic emphasis makes it distinct from an introductory self-study course.
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For readings linked to an active course context: Seoul National University
The Seoul National University Introduction to Machine Learning page says the course has no required textbook and links readings and notes through its schedule. This is useful if you want course-associated material without centering your study on one book. The schedule is term-specific and may change, so check the page for the current offering’s materials.
How to choose the right resource
- If you are new to the subject: begin with an explicitly introductory option, such as LMU’s undergraduate section or the gentler introduction identified by UW. Check the material’s assumed background before starting.
- If you want structure and practice: LMU is the clearest fit in this selection because the course describes videos, quizzes, exercises with solutions, and notebooks alongside its slides.
- If you prefer reading: choose a textbook listed with online or PDF access, and use the course pages to understand its context rather than treating a syllabus list as a complete review.
- If you want graduate-level material: MIT’s cited courses are explicitly graduate offerings. The 18.409 course is focused on algorithmic aspects; the 6.867 notes come from a Fall 2006 offering.
- If currency matters: note the source’s term or publication date. The cited MIT offerings are from 2006 and 2015; the UW reference page is for Spring 2026, and Tufts’ resource page is Fall 2025.
- If you need solutions or notebooks: verify that the specific resource provides them. LMU explicitly lists exercises with solutions and notebooks; the other pages cited here describe different materials.
What “free” means for these resources
The cited pages establish online, browser, PDF, or course-resource access for the items they identify. They do not establish that print editions are free, that every linked edition has the same access terms, or that all materials may be redistributed or reused. Check the individual resource’s terms if you need more than personal online study.
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