Udacity Computer Vision Nanodegree Review (2026): Is It Worth It?

Written by Sebastian Escalante Ccoyllo, who completed the Udacity Computer Vision Nanodegree in July 2022. Program facts, prices and the 2026 sections below were updated on 24 October 2026 by Josh Hutcheson, OnlineCourseing editor, from Udacity’s program page. See our review methodology. If you enroll through our links we may earn a commission, which never changes our verdict.

See the Computer Vision Nanodegree →

In this Udacity Computer Vision Nanodegree Review, I will be reviewing the Udacity Computer Vision Nanodegree that I completed in July 2022.

First of all, I will explain my background and the circumstances that led me to take the Nanodegree.

I’m a senior electrical engineering student at UTEC, a Peruvian university, at the time of writing this article. 

In my short professional experience, I’ve worked as a research intern in the Quantum Matter and Devices lab at Purdue University and also as a Software Developer at a Brazilian airline called Azul.

Also, I’ve co-founded two ventures while studying. The first one is a tutoring platform for freshmen STEM students called Luteach, here I’ve been mostly involved in the backend side of the product, such as developing server-side code in python and managing AWS services. 

Working on it, I developed my python skills enough to be confident to take advanced courses like the one I’m reviewing. 

I’m also involved in a startup that leverages computer vision to recognize vehicle license plates to secure Neighborhoods.

In my junior year I was required to make a project on my own about any topic I wanted, at that moment I took a Digital Image Processing class that really caught my attention. 

This project led me to use classical image techniques to detect license plates, and later to propose that topic as my senior undergrad thesis.

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My Motivation

With this background, I realized that I needed to use advanced Machine Learning topics to achieve my goal, since classical Image Processing techniques don’t suffice the requirements for such a task, mainly, due to the dependence on feature engineering. 

I started researching for online programs such as Coursera’s Deep Learning Specialization course. 

I took the first week, and it was more theoretical and focused on fundamentals rather than a practical approach. 

Also, one difference between both programs is that Coursera’s uses TensorFlow and Udacity, PyTorch, which both are popular python libraries used in the AI community. 

I had a preference for PyTorch since according to some people that have used both, the latter has a better API for designing your neural nets for getting started in this world. 

Don’t take my word for it! Listen to these guys and do more research.

Syllabus of the Udacity Computer Vision Nanodegree

There are several online courses accessible to study computer vision. Additionally, one of the well-liked courses to learn computer vision is the Udacity Computer Vision Nanodegree Program.

The Nanodegree has three modules. So let’s cover all of them one by one on this Udacity Computer Vision Nanodegree Review.

The first part of the course covers Computer Vision fundamentals and gives you a notion about how feature engineering works, it was a great refresher of how to apply common Image Processing techniques like segmentation, filtering, feature detection (all this with the OpenCV python library).

Some interesting topics covered are face detection using Haar Cascades and Image Segmentation using K-means Clustering. 

Which are good Image Processing techniques that don’t require neural networks to work, just really well-thought algorithms which run unsurprisingly well on just CPU.

Then, it briefly introduces you to neural networks, since this is a prerequisite to taking the course. 

You get to build two mini-projects before the main one. Additionally, there are really useful extra resources and more projects to build apart from the main syllabus.

This is great since the more projects and cases you are exposed to, you’ll get a better intuition on how to tackle future problems.

One thing I liked about the program was that they give you starting points for going deeper in your understanding, references of books, blog posts, and papers. 

The last ones are quite important because you will need them to build your nets from scratch and have an intuition about how to design them.

Project 1: Facial Keypoint Detection.

The project of this module consists of detecting up to 68 key points in faces.

Before starting with this project, I took the elective of Training A Neural Network because I didn’t have enough background to start working with neural nets right off the bat. 

If you don’t have previous knowledge about the Nanodegree you still can make it with the extra modules but be mindful of the extra time it will take to learn that first and finishing the projects.

Udacity is great at giving you extra resources at the right time, before going through a complex subject there are many resources that you can get help from.

This project is a great test of your abilities up to this point, since it encourages you to apply the basic image processing techniques for preprocessing the dataset and neural networks to actually find the key points on faces. 

Though it requires you to do your own research on which are the best network architectures for achieving this.

Completing the training of the model if this project took me around 3 days and 3 more to test it several times with different architectures. 

If you want to get the best performance for your models, it may take you a little bit more time since training such a large net is slow even using a GPU.

Project 2: Automatic Image Captioning.

At this point of the Nanodegree, everything seemed to flow just fine, but some topics here felt rushed, like Hyperparameters and Image captioning. 

Not all of them had practice notebooks such as the previous module and there were only short videos explaining complex neural nets, even though the animations were great I felt like a more practical approach would’ve been better for explaining them. 

The rest of the topics presented formulas and good intuitions on the design of the network. You’ll learn about RNN’s, LSTM’s, GRU’s and attention mechanisms needed for the project. 

These networks are special because they have memory, and because of that they are able to make relations between the data you feed them. 

Some applications that may sound common to you are text recommendations and weather forecasting. But now you will apply them in the context of Image processing. 

Before the project, you’ll have an explanation of how you can leverage a network used to translate text from one language to another to use as a net for translating images to text.

The Automatic Image Captioning is the most complicated project and the coolest (in my opinion), here you have to study the background of the project really well since there are some concepts that are not so intuitive (word embeddings) and I had to look from other sources and also the Knowledge platform which is something like Udacity’s own Stack Overflow, where only mentors answer your questions.

Knowledge is great because most of the problems you’ll face will be answered by a mentor on the same day (at least that’s what I saw) I didn’t take any questions because the ones I had were already answered.

Whenever a part of your program fails, there is a high chance that someone had that issue and asked it on the platform.

Also, probably you’ll run out of GPU compute time, but you can ask for more, I sent them a request, and they kindly gave me 128 h of GPU compute time from the initial 24h. 

So I suggest experimenting as much as you can and try the model after a few hours of training (2h to 3h).

There were a lot of subtleties that aren’t mentioned anywhere in the course (neither in the papers they give you) and you kind of have to stumble to the solution. 

It took several tries and bugs before the model runs correctly and gave some answer other than a man standing in the snow in all the pictures.

It was really frustrating, but I would advise reaching for help as soon as you can, it took me around a 1 week and a half to get decent results.

Project 3: Landmark Detection & Tracking.

The last module of the Nanodegree didn’t make much sense to me, since the focus is changed from Computer Vision to Robotics and probabilities. 

I really hoped for more content on LSTM’s, RNN’s, and other applications of that. It feels disconnected from what you’ve been doing with neural networks, but still is well explained and has lots of exercises for you to practice.

The content was by far the best explained, it’s done by one of the founders of Udacity Sebastian Thrun, who guides you through the implementation of Graph SLAM (short for Simultaneous Location and Mapping). 

You will have time to revise the basic concepts of material algebra, which are used to compute predictions and algorithms like the Kalman Filter and SLAM.

I believe that the topics presented could be useful as an introduction of how to track objects in images, but that isn’t presented.

The project here is a bit more complex implementation of what they teach you in the lessons, you’ll locate a robot on a map using only the inputs of its movement and the landmarks it sees. 

At this point, you would have already built a SLAM that works in 1D, and now they ask you to do the same but for 2D. 

Once you get the basics, this project is like a walk in the park. Apart from that, gives you great information about best practices and how to debug applications with python.

Udacity Computer Vision Nanodegree Project experience

Talking about the projects in this Udacity Computer Vision Nanodegree Review. Overall I found the projects very challenging, especially if you want to go the extra mile and polish them, there are an infinite amount of improvements to make. 

When you get the feedback of your projects (they take around 1 day to give you the results), the reviewer presents you with several resources and ways you could’ve improved your results, Let it be better preprocessing or changing completely the neural network. 

They were all worthy since I learned a lot from them, but I still need to complete the extra material that covers much more topics and other projects such as the skin cancer detector or how to deploy your models in the cloud.

Thoughts on Course Pricing and Timeline

Coming to the course pricing in this Udacity Computer Vision Nanodegree Review, I enrolled in this program in December 2020, I got the bundle subscription at $254.25 with a 75% discount due to a promotion (I enrolled in the Cloud Developer Nanodegree too, but if I would advise you against enrolling in more than one at the time, I did it because I would only have time to do it on summer and not while attending college).

To finish this Nanodegree took me 7 months. 

It could have been less, but I wasn’t 100% invested in it since I was working on two side projects as a backend and DevOps developer, enrolled in another Nanodegree, and then college started consuming most of my time.

You can’t pause a bundle subscription, but if you pay on a per-month basis you can. Luckily, I tried to cancel the subscription and the customer service of Udacity offered me to pay $75 per month if I needed more time.

So after my bundled subscription ended I paid for two months at $100 each, and after I tried to cancel it I got the offer at $75.

I would say that the price (at 75% discount, at full price is not worth it) is justified given the services they offer you like project mentor reviews, GitHub, and LinkedIn profile reviews, and career counseling (now deprecated, but I managed to have one meeting before they removed it). 

Furthermore, there is more material inside apart from the main syllabus that you can follow when you want. 

Most of the code inside the course can serve you as boiler plates for future projects, and there are very useful functions to get started with commonly used nets such as YOLO.

The bundle subscription gives you 3 months however considering that you have other obligations as myself you may need up to 4 months to finish this Nanodegree, otherwise, you should be able to complete it on time if you meet the requirements of the course such as having an understanding of python at an intermediate or advanced level, neural network architectures and worked with a framework like PyTorch, TensorFlow, or Keras.

Must see -> Is a Udacity Nanodegree worth it? Our full review

Pros and Cons

Now, let’s discuss some of the pros and cons of this Computer Vision Nanodegree in this Udacity Computer Vision Nanodegree Review.

Pros of the Udacity Computer Vision Nanodegree

Gives you a great background on most of the topics of Computer vision and great material too.

Mentors give valuable resources after each review.

The knowledge community is always active and can have your questions answered really fast.

The extracurricular content covers much more than the core Nanodegree, and it is worth studying it.

Cons of the Udacity Computer Vision Nanodegree

The second module felt short of content. Being an advanced course, you could expect more detail on advanced networks and more examples (in the core syllabus), though that can be solved taking the extracurricular projects.

The last module felt disconnected from the course and may not interest you as much as the previous, part of your motivation is only about computer vision.

The bundled subscription only gives you three months, but for really diving deep into the most complex topics, they should bundle it for four months.

Check this out -> Udacity Artificial Intelligence Nanodegree review

Conclusions

The course does a great job with the basics of computer vision and some advanced topics, but fails to deliver more profound explanations with practical exercises on its proposition as an advanced course. 

Although, it contains valuable short videos and useful code and functions to get started with any project you’d like to make in the future.

I would recommend this course to anyone interested in Computer Vision and Robotics too, since the last module of the course covers robotics-related topics and the extracurricular modules make the overall investment worth it.

The Computer Vision Nanodegree in 2026: what has changed

We re-read the program page on 24 October 2026 with every course outline expanded. The structure Sebastian describes is intact, and so are all three projects.

Two of Sebastian’s complaints have been addressed by Udacity’s billing changes. The bundle now covers four months rather than three, which he said the program needed. And you can now pause a subscription for one to three months without losing your classroom progress, instead of paying for months you cannot use or negotiating a discount to stay.

What the Computer Vision Nanodegree costs in 2026

Udacity no longer sells programs one at a time by default. A subscription opens the whole catalog, and you can also buy a single Nanodegree outright. Today’s prices, which update automatically when Udacity changes its offer:

• Monthly: $130 a month (48% off the $249 list price during the current sale, with code NOV48).
• 4-month bundle: $439.92 for 4 months, then month to month.
• One-time purchase: $519.48 for nd891 alone, kept until you complete it or Udacity phases the program out.

At Udacity’s 37-hour estimate, someone with the prerequisites studying ten hours a week needs about a month. Sebastian took seven months alongside college and two side projects, and his GPU training runs took days, so budget two months unless you can study full time. Refunds are only available within seven days of your first purchase (14 in the EU). One more change from 2022: on subscriptions bought after September 2023, you lose access to the classroom when the subscription ends, even after you graduate, although you keep access to the projects you submitted. Download your notebooks before you cancel.

Check today’s Computer Vision price →

Is the syllabus still current?

For the fundamentals, yes. Image filtering, feature detection, CNNs, region-based detectors like Faster R-CNN, YOLO and CNN-RNN captioning are still the right way to learn how computer vision works, and the facial keypoint project is a good test of whether you understand it. The SLAM and Kalman filter material, taught by Sebastian Thrun, is unusual in a computer vision course and valuable if you are heading toward robotics or autonomous vehicles.

What it does not teach is the last few years of the field. Attention appears only as an optional lesson, and there is nothing on vision transformers, diffusion-based image generation or image segmentation models. Udacity’s own Applied Generative AI Engineering Nanodegree, updated on 20 October 2026, lists computer vision transformers, image segmentation and diffusion models among its skills. If your goal is to work on current vision systems rather than to understand the classics, plan to learn those topics on top of nd891, or start with that program instead.

What other learners say about the Computer Vision Nanodegree

The program page shows 478 reviews averaging 4.7 out of 5. The recent ones echo Sebastian’s account closely. Positive reviews praise the projects and say the course gives a real understanding of how CNN-RNN models work. The critical ones repeat his two complaints: the SLAM module felt off-topic to learners who wanted deep learning throughout, and mentor questions sat in a queue too long. One December 2025 review simply calls it “very difficult”.

Across all programs, Udacity holds 4.5 out of 5 on Trustpilot from 3,414 reviews, where the most common complaint is slow project reviews. On Reddit, the recurring point is that you are paying for structure and human feedback; the raw material on CNNs and detection is available free elsewhere.

Alternatives to the Udacity Computer Vision Nanodegree

If you are not sure you have the prerequisites, the beginner-level AI Programming with Python program covers Python, NumPy, PyTorch and neural network basics first. For a broader look at the field, see our deep learning course guide.

Frequently asked questions

How much does the Udacity Computer Vision Nanodegree cost?

Today it costs $130 a month on the monthly subscription, $439.92 for 4 months as a 4-month bundle, or $519.48 to buy the program outright. Udacity estimates 37 hours, so most learners need one or two months of subscription.

How long does the Computer Vision Nanodegree take?

Udacity estimates 37 hours across three core courses of 12, 9 and 15 hours plus optional lessons. Training the project models on a GPU adds time; our reviewer took seven months part time, mostly because of other commitments.

Is the Computer Vision Nanodegree good for beginners?

No. It is rated advanced and assumes object-oriented and intermediate Python plus basic probability, and you build neural networks from the first project. Beginners should start with AI Programming with Python.

Is the Udacity Computer Vision Nanodegree still updated?

Its last update was 30 May 2025. It still teaches CNNs, YOLO, RNNs, LSTMs and SLAM, but not vision transformers or diffusion models, which Udacity covers in its Applied Generative AI Engineering program.

What projects are in the Computer Vision Nanodegree?

Three: Facial Keypoint Detection (a CNN that finds points on faces), Image Captioning (a CNN encoder with an RNN decoder) and Landmark Detection & Tracking (implementing SLAM).

Do you keep access after you graduate?

Not on a subscription bought after September 2023: classroom access ends when the subscription does, although you keep access to the projects you submitted. A one-time purchase stays available until you complete the program or it is phased out.

See the Computer Vision Nanodegree →

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