CS Video Courses: Free Computer Science Courses Online
/cs-video-courses
CS Video Courses is a plain markdown list linking to full university computer science lectures on YouTube and OpenCourseWare — not a hosted platform with quizzes or certificates. It's worth using once you can structure your own study plan: you get raw lecture playlists from MIT, Stanford, Harvard, and dozens of other schools, but zero guidance on sequencing them. A guided curriculum with checkpoints isn't part of the deal.
What is CS Video Courses?
CS Video Courses is a GitHub README that catalogs links to full university computer science lectures, grouped into sections like Introduction to Computer Science, Data Structures and Algorithms, Machine Learning, and Systems. Each entry points to an external video source — a YouTube playlist, an OpenCourseWare page, or a university's own lecture-capture site — rather than hosting any video itself.
What the collection covers
- ✓Full semester-length playlists, not highlight clips — courses like MIT's 6.006 Introduction to Algorithms or Stanford's CS106A appear as complete lecture series with dozens of videos each.
- ✓Core theory is covered in depth: algorithms, data structures, computer architecture, and database systems each get their own dedicated section.
- ✓Specialized tracks go beyond intro CS — computer vision, robotics, quantum computing, reinforcement learning, bioinformatics, and computational physics all have topic tags in the repo.
- ✓Sources span dozens of schools: MIT OpenCourseWare, Stanford's SEE program, Harvard's CS50, UC Berkeley, Georgia Tech, and international programs like IIT's NPTEL lecture archives and TUM's live-lecture platform.
- ✓Systems and security get their own lanes — distributed systems, embedded systems, and security appear as separate topic tags rather than being lumped into one generic 'systems' bucket.
- ✓Every entry is a bare link with a course code, title, and school — no annotation for difficulty, prerequisites, or how many hours the playlist runs.
Who benefits most from this list
- •A self-taught developer building a CS foundation without a degree, using the algorithms and data structures sections in place of a bootcamp's shortened version.
- •A working engineer who wants the specific lecture on a topic — say, distributed systems, systems programming, or computer vision — without enrolling in a full course or paying for a MOOC.
- •Someone assembling their own MIT OCW-style curriculum who wants a single index instead of hunting across a dozen university course-catalog pages.
- •A student prepping for grad-level topics like advanced algorithms or algorithmic game theory who wants to see how different professors teach the same subject.
Strengths
- ✓The CONTRIBUTING policy rejects small MOOCs and basic tutorials on sight, so what's in the list skews toward genuine college and university semester courses rather than marketing content.
- ✓Breadth is real, not just intro material — grad-level and research-adjacent topics like advanced algorithms, algorithmic game theory, and quantum computing sit alongside the intro sections.
- ✓Multiple universities' takes on the same subject sit side by side, so you can compare MIT's and Stanford's algorithms courses instead of being stuck with one professor's style.
- ✓It's free and requires no sign-up — every link goes straight to a public video source.
What this list does not cover
- △It's a plain link list — no progress tracking, no exercises graded for you, and no certificate at the end, unlike a MOOC platform.
- △Entries aren't annotated with difficulty, prerequisites, or estimated time, so you have to click through each course page to know what you're getting into before committing hours.
- △Video sources are external (YouTube, OpenCourseWare, individual university sites), so playback quality, caption availability, and long-term link stability depend entirely on each school, not on this repo.
- △There's no single recommended path through the list — Introduction to Computer Science and Data Structures and Algorithms are just two of many sections, and building a full curriculum out of them is left entirely to you.
Other free CS learning resources
Common questions about CS Video Courses
CS Video Courses costs nothing — it's a public GitHub list of links to free video lectures hosted on YouTube, OpenCourseWare, and university sites, so there's no subscription or paywall to access any of it.
CS Video Courses includes multiple MIT OpenCourseWare courses and several Stanford lecture series, alongside Harvard, UC Berkeley, Georgia Tech, and international programs like IIT's NPTEL, spread across its Introduction to Computer Science and Data Structures and Algorithms sections.
CS Video Courses covers algorithms, data structures, computer architecture, databases, machine learning, computer vision, robotics, security, embedded systems, quantum computing, and reinforcement learning, based on the topic tags in the repository.
The update frequency isn't stated in the CS Video Courses README — the repo accepts community pull requests for new courses under its CONTRIBUTING guidelines, but there's no published schedule for how often entries are added or checked.
CS Video Courses is restricted to college and university-level courses — the CONTRIBUTING guidelines explicitly say not to add small MOOCs, basic tutorials, or promotional channel links, so entries are full semester courses rather than short beginner tutorials.
You can build a curriculum from CS Video Courses, but you'll design the sequence yourself — the list groups lectures by topic (algorithms, systems, machine learning, and more) without recommending a course order or marking prerequisites between sections.
Who should try it — and who should skip
Try CS Video Courses if you already know roughly what a CS curriculum looks like and just want self-paced CS learning from the original lecture instead of a MOOC's cleaned-up version — self-taught developers, CS students supplementing a weak lecture, and anyone chasing an advanced topic like distributed systems or computer vision will get the most out of it. Skip it if you need structure imposed on you: there's no suggested order, no exercises, and no way to know if you've covered enough before calling yourself done. Complete beginners who've never written a line of code should start somewhere with hands-on exercises instead, like freeCodeCamp.
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