The best free MIT AI courses, curated
MIT gives away an extraordinary amount of its teaching — full lecture videos, slides, problem sets and code — through MIT OpenCourseWare and open course sites. Below are the AI, machine-learning and foundational courses worth your time, organized into a path from a gentle Python on-ramp through deep learning, with links straight to MIT.
These are curated links to publicly available course materials published by MIT — via MIT OpenCourseWare (licensed CC BY-NC-SA) and open MIT course sites. All content belongs to its respective authors and MIT. AnybodyCanAI is not affiliated with, sponsored by, or endorsed by MIT. The MIT name is used only to identify the source of each course.
Introduction to Deep Learning
MIT's fast-paced, fully open deep learning bootcamp
Alexander Amini, Ava Amini · Spring 2026 · EECS
Summary
MIT's flagship introductory deep-learning course: neural networks from the ground up, CNNs, sequence models, generative modeling, reinforcement learning and LLMs — with three hands-on software labs and a final project.
The most accessible serious deep-learning course anywhere — every lecture, slide deck and lab is open-sourced under the MIT license and posted to YouTube each year. A perfect, current first dive into building models.
Schedule
Lecture topics and dates rotate each offering — we link straight to MIT's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
Lectures are posted to YouTube each year; slides and all lab code are open-sourced on GitHub under the MIT license.
Materials & links
Source: MIT — all links open on the provider's own site.
Artificial Intelligence
Patrick Winston’s celebrated AI lectures
Patrick H. Winston · Fall 2010 · EECS
Summary
A broad tour of classical AI: search, constraint satisfaction, reasoning, rule-based systems, and foundational machine learning — taught with the clarity that made these lectures legendary.
Patrick Winston's full video lectures are a beloved, timeless introduction to how AI thinks about problems. Great grounding in the ideas beneath modern ML, and completely free on OpenCourseWare.
Schedule
Lecture topics and dates rotate each offering — we link straight to MIT's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
Full lecture videos, assignments and exams are public on MIT OpenCourseWare under a Creative Commons license.
Materials & links
Source: MIT — all links open on the provider's own site.
Introduction to Computer Science and Programming in Python
Zero-to-programming, no experience needed
Ana Bell, Eric Grimson, John Guttag · Fall 2016 · EECS
Summary
MIT’s on-ramp to programming for people with little or no experience: writing, testing and debugging Python, plus the computational-thinking habits every AI practitioner relies on.
The prerequisite behind almost everything else here. If code feels intimidating, start with this — full videos, lecture slides and code, and assignments are all public.
Schedule
Lecture topics and dates rotate each offering — we link straight to MIT's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
Lecture videos, lecture slides and code, and assignments are public on MIT OpenCourseWare under a Creative Commons license.
Materials & links
Source: MIT — all links open on the provider's own site.
Linear Algebra
Gilbert Strang’s classic course
Gilbert Strang · Spring 2010 · Mathematics
Summary
The linear algebra every ML practitioner needs: systems of equations, vector spaces, determinants, eigenvalues and positive-definite matrices — taught in Gilbert Strang’s famously intuitive style.
Linear algebra is the language of machine learning, and Strang’s lectures are the reference version — full videos, problem sets and exams, all free on OpenCourseWare.
Schedule
Lecture topics and dates rotate each offering — we link straight to MIT's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
Full lecture videos, problem sets and exams with solutions are public on MIT OpenCourseWare under a Creative Commons license.
Materials & links
Source: MIT — all links open on the provider's own site.
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
Linear algebra, applied to deep learning
Gilbert Strang · Spring 2018 · Mathematics
Summary
The applied sequel to 18.06: how linear-algebra ideas — factorizations, the SVD, optimization and gradient descent — power modern machine learning and neural networks.
Bridges the gap between "I know some linear algebra" and "I understand why deep learning works." Strang’s full video lectures and assignments are public on OpenCourseWare.
Schedule
Lecture topics and dates rotate each offering — we link straight to MIT's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
Full lecture videos and assignments are public on MIT OpenCourseWare under a Creative Commons license.
Materials & links
Source: MIT — all links open on the provider's own site.
Machine Learning for Healthcare
Real-world ML where the stakes are high
Peter Szolovits, David Sontag · Spring 2019 · EECS
Summary
Machine learning applied to clinical data: risk stratification, disease-progression modeling, precision medicine, diagnosis and improving clinical workflows — a grounded look at ML in a high-stakes domain.
A standout example of ML applied to a hard, consequential real-world field. Full lecture videos and notes are public on OpenCourseWare — great for seeing how the theory meets practice.
Schedule
Lecture topics and dates rotate each offering — we link straight to MIT's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
Lecture videos and lecture notes are public on MIT OpenCourseWare under a Creative Commons license.
Materials & links
Source: MIT — all links open on the provider's own site.