Computer Vision Nanodegree
by Udacity Team · Udacity
Our Verdict
Worth it — with caveatsUdacity's Computer Vision Nanodegree (nd891) is a worthwhile but premium intermediate-to-advanced program for learners who already know Python and the basics of neural networks and want hands-on, project-based computer vision experience in PyTorch. Based on the official syllabus and aggregated public student feedback, its core value lies in three substantial graded projects (Facial Keypoint Detection, Image Captioning, and SLAM/Landmark Detection) plus mentor-reviewed feedback rather than the certificate itself. The official Udacity page lists a 4.7/5 rating (478 reviews) and was last updated May 30, 2025, and independent reviewers corroborate strong instructor quality and a good theory-practice balance. The main drawbacks are the steep $249/month subscription price, real prerequisite demands, and the fact that some advanced material (Module 2) feels rushed while the SLAM module leans toward robotics over classic vision. It is best taken in a focused 1-2 month sprint by someone with company reimbursement or a heavy discount, and skipped by true beginners.
Strong, project-driven curriculum with credible instructors and mentor feedback, but the $249/month price, genuine Python/ML prerequisites, and a robotics-heavy final module make it worthwhile only for intermediate learners who can commit time and afford it (ideally via discount or employer reimbursement).
Best for: Intermediate learners who already know object-oriented Python and the fundamentals of neural networks, want structured, mentor-reviewed practice building CNN/RNN computer-vision projects in PyTorch, and value hands-on portfolio projects (facial keypoints, image captioning, SLAM) over self-paced video alone. A particularly good fit for people whose employer reimburses tuition or who catch a steep Udacity discount, and for those interested in both computer vision and robotics/localization.
Skip if: Complete beginners without Python, statistics, or any deep-learning background (multiple reviewers explicitly say it is 'not worth it for beginners'); budget-conscious self-funders who can get equivalent skills cheaper or free (e.g. fast.ai or Stanford CS231n); and anyone seeking a credential that is itself a hiring differentiator, since reviewers agree the certificate alone won't get you a job without additional portfolio work.
About This Course
Implement object detection, image captioning, and SLAM using CNNs, RNNs, and localization techniques with PyTorch.
What You'll Learn
Curriculum
Image representation and classification, convolutional filters and edge detection, types of features, image segmentation, CNN layers, and feature visualization. Project: Facial Keypoint Detection using CNNs.
Advanced CNN architectures (Faster R-CNN), YOLO multi-object detection, RNNs and LSTMs, optional attention mechanisms, and image captioning. Project: train a CNN-RNN model to caption images.
Motion and optical flow, robot localization with Bayesian filters, 2D histogram filter mini-project, Kalman filters, matrix transformations, and SLAM. Project: implement SLAM for landmark detection and tracking.
Program orientation, optional AWS GPU cloud setup, a Training Neural Networks review (PyTorch), and extracurricular modules (applications of CV, skin cancer detection, text sentiment analysis, semantic segmentation, and a long optional C++ programming track).
Prerequisites
- Intermediate Python and object-oriented programming proficiency
- Basic neural network / deep learning familiarity (ideally PyTorch)
- Basic probability, statistics, and arithmetic / linear algebra
- Fluent written and spoken English
Instructor
Udacity Team
Instructor · Udacity
Pros & Cons
Pros
- Three substantial, real-world graded projects (facial keypoints, image captioning, Graph SLAM) that produce genuine portfolio pieces
- Mentor and project-reviewer feedback is repeatedly praised as fast (often within ~1 day) and actionable, with helpful resource suggestions
- Credible instructor lineup including Udacity founder Sebastian Thrun, Cezanne Camacho, and Luis Serrano, with a solid theory-to-practice balance
- Covers modern and end-to-end topics (YOLO, attention, CNN+RNN captioning, Kalman filters, SLAM) rather than only classical image processing
- Generous extracurricular material that extends value beyond the three core projects
Cons
- Premium pricing at $249/month (or $846 for a 4-month bundle); multiple reviewers say full price is 'not worth it' and recommend waiting for a discount
- Real prerequisite demands make it unsuitable for beginners without Python, statistics, and prior neural-network knowledge
- Reviewers note the advanced deep-learning module can feel rushed, and the SLAM module leans toward robotics and feels somewhat disconnected from core computer vision
- Course access is tied to an active subscription, so you lose the learning materials once your subscription lapses; the certificate alone is not a strong hiring signal
Alternatives To Consider
Frequently Asked Questions
Is Computer Vision Nanodegree free?
Computer Vision Nanodegree is $249/mo. Subscription-based: $249/month, or $846 for a 4-month bundle (about 15% off), for access to Udacity's full catalog. No free audit of the full Nanodegree; a free 'Computer Vision Nanodegree Preview' exists on Class Central. Steep personalized discounts (one reviewer paid ~$254 total at 75% off) appear frequently, and value is far better with a discount or employer reimbursement.
Who is Computer Vision Nanodegree for?
Intermediate learners who already know object-oriented Python and the fundamentals of neural networks, want structured, mentor-reviewed practice building CNN/RNN computer-vision projects in PyTorch, and value hands-on portfolio projects (facial keypoints, image captioning, SLAM) over self-paced video alone. A particularly good fit for people whose employer reimburses tuition or who catch a steep Udacity discount, and for those interested in both computer vision and robotics/localization.
What will you learn in Computer Vision Nanodegree?
Represent and classify images, apply convolutional filters, and perform edge detection and feature extraction; Build and train CNNs in PyTorch and visualize learned features; Use advanced architectures (Faster R-CNN, YOLO) for multi-object detection; Combine CNNs with RNNs/LSTMs and attention to generate automatic image captions.
What are the prerequisites for Computer Vision Nanodegree?
Intermediate Python and object-oriented programming proficiency; Basic neural network / deep learning familiarity (ideally PyTorch); Basic probability, statistics, and arithmetic / linear algebra; Fluent written and spoken English.
Is Computer Vision Nanodegree worth it?
Strong, project-driven curriculum with credible instructors and mentor feedback, but the $249/month price, genuine Python/ML prerequisites, and a robotics-heavy final module make it worthwhile only for intermediate learners who can commit time and afford it (ideally via discount or employer reimbursement).
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on Udacity's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.
Sources
- Official Udacity Computer Vision Nanodegree page (syllabus, prerequisites, rating, instructors)
- MLTut independent review (pros/cons, prerequisites, who should skip)
- OscarLeo review by a 7-Nanodegree completer (project detail, module critiques, real pricing paid)
- Onlinecourseing 2026 review (pros/cons, recommendation)
- Udacity 2026 pricing reference ($249/month, $846 4-month bundle)