MITx / edX
MITx 6.86x — Machine Learning with Python Review (From Linear Models to Deep Learning)
MITx 6.86x is a genuinely rigorous, graduate-level machine learning course where you write the algorithms yourself in Python rather than calling a library. The content is MIT-deep — linear models, SVMs, neural networks, clustering, and reinforcement learning — and the auto-graded projects are widely praised. The honest costs are real: a heavy ~15-hour weekly load, terse lectures with few worked examples, and prerequisites in linear algebra, calculus, probability, and Python that you cannot skip. Outstanding if you want deep understanding; frustrating if you wanted a quick, library-first tour.
Final score
from 30 analysed opinions
Published AI-researched, editor-audited
Distribution of opinions
Per-criterion scores
Graduate-level MIT curriculum: linear classifiers, SVMs, neural nets, clustering, recommender systems, and reinforcement learning, taught from first principles. Reviewers praise the depth and the under-the-hood focus, though several find the lectures terse with too few worked examples.
Taught by MIT faculty Regina Barzilay, Tommi Jaakkola, and Karene Chu. Strong expertise, but learner feedback on the lectures is polarized — praised for intuition by some, called short and example-light by others. Most learning happens through the projects, not the videos.
A verified certificate (~$300) buys MIT-grade material that builds algorithms from scratch and counts toward the Statistics and Data Science MicroMasters. The course can also be audited for free, so the paid tier is mainly for the credential and graded autograder access.
As a self-paced MOOC there is no 1:1 instructor support; help comes from course forums and learner-run Discord groups. Multiple reviewers explicitly recommend joining a class Discord to stay motivated and unblock on projects, which signals the official support channel alone is thin.
You implement linear models, kernels, neural nets, and RL by hand, which builds durable intuition for how ML actually works. The trade-off, noted by reviewers, is that it deliberately avoids high-level libraries like scikit-learn, so it is foundational rather than a job-ready tooling course.
What learners said
What people loved
6- You build ML algorithms from scratch — linear models, SVMs, neural nets, reinforcement learning — instead of just calling a library function×14
- The auto-graded Python projects (review analyzer, digit recognition, RL) are widely praised as hands-on and remarkable×11
- Genuine MIT graduate-level depth and rigor, taught by real MIT faculty, that counts toward the Statistics and Data Science MicroMasters×10
- Strong at building intuition for the theory and math behind algorithms, not just surface usage×8
- Can be audited for free, so you can sample the full content before paying for the verified certificate×6
- An active learner community (course forums and class Discord groups) helps you stay motivated and unblock on projects×5
What frustrated learners
5- Heavy time commitment — roughly 15 hours a week because the biweekly projects are extensive×12
- Lectures are short and terse with few worked examples; several reviewers found them weak and example-light×9
- Steep prerequisites in linear algebra, calculus, probability, and Python — not a beginner course despite the "with Python" title×9
- Deliberately avoids high-level libraries like scikit-learn, so it is foundational rather than job-ready tooling×6
- Limited official support; real help comes from learner-run Discord groups rather than the platform×5
Real quotes from real users
“The course was somewhat traditional, but the auto-graded python projects were remarkable. This course was still about 15 hours a week because the projects were extensive.”
“I highly recommend joining a class Discord chat because I found that to be motivating and helped give me ideas when I found myself stuck on part of a project.”
“Unlike many online courses that teach you how to call a function, MIT forces you to write the function.”
“Low-level understanding of machine learning with Python programming. It's Python and machine learning but not how to use the high-level API of scikit-learn. The focus is how machine learning algorithms work under the hood.”
“I'm taking the Micromasters in Statistics and Data Science from MIT. It's awesome. It's very rigorous though.”
“The quality of instruction is high and each covers a lot of ground so I thought they were worth it. I definitely perceive the skills I'm acquiring as valuable.”
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How we evaluated this
This review synthesizes 30 opinions collected across the public web. Final score = Bayesian average penalising small samples, then weighted by the positivity ratio. No paid placements, no hidden agenda.
- 16 from Blogs
- 7 from Hacker News
- 7 from Official course platform