Bottom line: MIT’s 6.S191, Introduction to Deep Learning, is a free MIT course that compresses modern deep learning — neural networks, computer vision, generative AI, reinforcement learning and large language models — into a short lecture-and-lab series. It costs $0 and offers no certificate. It is an excellent fast on-ramp if you can code in Python; it is not a hand-holding course for absolute beginners.
What is MIT 6.S191?
6.S191 is MIT’s undergraduate introduction to deep learning, taught during MIT’s January Independent Activities Period and published openly through MIT OpenCourseWare and the course site introtodeeplearning.com. The MIT instructors listed for the course are Alexander Amini and Ava Soleimany. Recent editions list nine lectures and three labs, with lecture videos, slides and lab materials shared freely.
The arc is deliberately fast: foundations of neural networks and deep learning first, then sequence and vision models, generative modelling, reinforcement learning and language models, with labs where you build and train models yourself. Because it is a real MIT course released openly, you get the genuine lectures and assignments rather than a simplified remake — and also a university course’s pace and assumptions. This review is research-based; we did not take the course.
How much does MIT 6.S191 cost?
| Option | Price | Notes |
|---|---|---|
| Full course materials online | $0 (free) | Lectures, slides and labs published openly via MIT |
| Certificate | Not offered | No verified certificate or credential is listed for the open version |
| Software | Free tools | Labs use standard open-source Python deep-learning tooling; you may need a computer able to run notebooks or a free cloud notebook account |
| Print textbook | None required | The course is video, slides and labs — there is no required paid book listed |
What’s good
- Completely free: A current MIT deep-learning course at no cost, with no upsell wall.
- Modern syllabus: Covers transformers-era topics — generative AI and large language models — not just classic networks.
- Short and dense: Nine lectures and three labs is a realistic finish line compared with semester-long courses.
- Builds, not just watches: Labs have you train models, which is where the concepts actually stick.
- Trusted source: Real MIT teaching materials, openly licensed through OpenCourseWare.
What’s not so good
- Assumes coding: You need working Python and comfort with basic maths notation; true beginners will struggle.
- No certificate: Nothing official to show an employer — your evidence is what you build afterwards.
- No support or deadlines: Self-study means no grading, no office hours and no cohort to keep you on schedule.
- University pace: Lectures move quickly and expect you to pause, rewatch and do the labs seriously.
Who is MIT 6.S191 best for?
Programmers, data-curious engineers and students who want the fastest credible route from “I use AI tools” to “I understand how deep learning works.” If you cannot code yet, learn Python basics first, then come back — the course will reward you. If you need a certificate for a job application, pair it with a credentialed course elsewhere and use 6.S191 for the understanding.
Frequently asked questions
Is MIT 6.S191 really free?
Yes. MIT publishes the lectures, slides and labs openly at no cost through its course site and OpenCourseWare.
Do you get a certificate from 6.S191?
No certificate is listed for the open version. It is a learning resource, not a credential.
How long is MIT’s Introduction to Deep Learning?
Recent editions list nine lectures and three labs, designed for MIT’s intensive January term — most self-learners spread it over two to four weeks.
What do I need before starting 6.S191?
Working Python, basic linear algebra and calculus familiarity, and a way to run Python notebooks.
Verdict
For anyone who can code, 6.S191 is close to a free lunch: current, rigorous MIT teaching with labs, at $0. Its only real costs are effort and the lack of a certificate. Take it to understand deep learning properly, then prove it with a project — that combination beats most paid certificates.
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