Self-Driving Car Engineer Nanodegree vs Machine Learning Specialization

Reviewers praise the breadth — CV, sensor fusion, localisation, planning, control, ROS on Carla. The caveat is the curriculum is deep-learning-heavy and some flag this as the wrong architectural bet for real autonomous vehicles.

Sebastian Thrun, David Silver and the rotating industry instructors (Mercedes, BMW, NVIDIA, Uber ATG, Waymo alumni) get steady positive mentions. Reviewers who took the free CS373 first describe the nanodegree as a paid extension.

The biggest drag on the score. Original 2016-2017 price was ~$2,400; current pricing sits around $249-399/month, total ~$1,000-1,500. Flagged against free MIT 6.S094, MIT 6.832 and Stanford CS221/CS231n alternatives.

Original cohorts received mentor-graded project reviews and praised them highly, but later reviewers — including one of the most-cited HN voices — report Udacity "got rid of this feature" for self-paced learners. Slack community partially compensates.

Projects are unusually applied — behavioural cloning, lane finding, sensor fusion, path planning, and a final integration on Udacity's real Carla vehicle via ROS. The gap is that industry has moved past the deep-learning-heavy approach taught.

Praised for intuitive explanations and the expanded neural networks unit, but reviewers note the new version trades depth for accessibility — backprop is brushed past, RL feels like a preview.

Andrew Ng's pedagogy gets near-universal praise across HN and blogs. Multiple commenters describe him as the best instructor they ever had; complaints are essentially absent.

Content is strong relative to cost, and auditing remains possible. The friction comes from Coursera's subscription gating around grading and certificates — a recurring HN gripe.

Browser-hosted Jupyter notebooks with auto-grading remove a major friction point from the original. The community forum is active but not deeply mentioned in reviews.

Builds a real foundation in ideas and Python tooling, but datasets are clean and deployment is out of scope. Reviewers flag the need to supplement with Kaggle or a portfolio project.