The 10 Best Online Courses for Computer Vision

How can you best approach mastering the area of computer vision? Our recommended online courses will fit all starting points and ambition levels.

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#1 Computer Vision Course for 2023:
Computer Vision Expert Nanodegree (Udacity x Affectiva & Nvidia)

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$399 $299/month
$1,197 $763 for 3-month access

With a 3-month curriculum designed with Nvidia together with Udacity's dependably high-quality delivery and mentoring and career support services, this Nanodegree is our overall pick for anyone looking to start or upgrade their career in the area of computer vision.

Course Instructor(s)

Sebastian Thrun; Cezanne Camacho; Alexis Cook; Juan Delgado, Jay Alammarl; Ortal Arel; Luis Serrano

Course Duration

3 months (at 10-15 hrs/week)

Platform Rating

4.8 / 5

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Can computers see the world as humans do? While we still don’t understand how human vision functions to be able to answer that question, the advent of more powerful artificial intelligence has catapulted computer vision – the field of study that tries to understand and replicate parts of the human vision system – to revolutionizing a range of industries as well as having an impact on our everyday lives. As a result, there is a large demand for engineers in computer vision and it is increasingly important also for non-specialists to have an understanding of this area.

Artistic illustration of bounding boxes used to train artificial intelligence on object recognition.
Artistic illustration of bounding boxes used to train artificial intelligence on object recognition.

In this article, we will look at how you can best approach mastering the area of computer vision, depending on your starting point and your goals.

We present our main recommendations for courses in greater detail below. Note that at the end of this review, you will also find a more comprehensive list of good alternative courses that, while they did not make our short list of recommended courses, may be a better fit for your specific learning needs.

Computer Vision - Table of Contents

Introduction

The field of computer vision has its roots in the early work on artificial intelligence at universities in the 1960s. Alongside the development of computers, computer vision has also been improving gradually. One of the greatest practical breakthroughs that many of us are familiar with was optical character recognition (OCR) in the 1970s – the possibility for computers to read text.

But even if computer vision had already come a long way through “conventional” computing, it has received much greater attention lately through the advancement of deep learning and neural networks, helping shift the focus to object recognition and its practical applications. In our current world, with vast amounts of visual data produced and shared daily (more than 3 billion images per day and 720,000 hours of video), improved artificial intelligence techniques that can be trained on this flow of information and the computing power needed for processing it, computer vision has become more powerful and accessible.

The applications for computer vision have grown exponentially as a result, with the software and hardware computer vision market expected to reach $48.6 billion in 2022. This will help transform several aspects of our economy and everyday life, from manufacturing processes via augmented reality and video surveillance to self-driving cars. To design and implement these new applications, there is a large and growing need for computer vision engineers – employers have increased their demand for AI-related roles by over 100% since 2019 – with salaries to match.

Study paths: How do I choose my online course?

As for any area of study, how best to approach this field depends on what your objectives are. For computer vision, these tend to fall in the following broad categories:

  • Aiming to specialize in computer vision: If you are an aspiring computer vision developer, you have an exciting path ahead of you, with several good options.
    • Follow our overall recommended course or our fundamentals recommendation, depending on your starting point and objectives. If you are not able to afford these options, follow our recommended free course.
  • Applying computer vision tools: Developers with other specializations can do a lot with modern computer vision tools that does not require them to have a deep understanding of the area.
    • Follow our recommended crash course, introductory lab, and course with the best coverage of different tools.
  • Strategic overview: Many will benefit from understanding the applications and implications of computer vision better, without necessarily looking to program anything yourself.
    • Follow our recommended high-level course.
  • Already a specialist: Those who are already working in computer vision will benefit from continuing education.
    • Follow our recommended course for experts or go through our full list to find the course that best suits your specific learning needs.

While some universities offer good face-to-face programs that cover computer vision, there is luckily also a wide range of online courses that you can complete remotely, while having a similarly high quality and allowing you to mix and match courses as needed to meet your objectives. We have taken a comprehensive look at the available online courses and have tested them out to provide our recommendations to aspiring students.

Prerequisites: What do I need to know before getting started?

As computer vision in its modern applications combines methods for image processing with artificial intelligence techniques, it is not a topic that is suitable for those who are complete beginners to programming, data science, statistics, and artificial intelligence. As they would otherwise be too big and have a too wide scope, most courses on the topic have prerequisite knowledge in these areas. While most of the courses on this list will not control that you have these prerequisites, it is strongly recommended that you make sure you have the following before moving forward, so that you can digest and learn from the course material:

  • Programming: Python is by far the most common programming language used in courses on computer vision, with intermediate programming skills recommended or required. In our list of best Python courses, our overall recommendation is Python for Everybody from Coursera (which a dedicated student can complete in a lot less time than the estimated 8 months). If you need a more intense and project-focused program with mentoring, we recommend one of the nanodegrees from Udacity – Programming for Data Science with Python or AI Programming with Python if you are completely new to Python, or the Intermediate Python Nanodegree if you already have basic familiarity with the language.
  • Math: You need knowledge in algebra and statistics in several data science fields, and it is particularly important in areas using artificial intelligence. If you need to brush up on your math skills, our recommendation is Brilliant – they have courses in a very engaging format on the topics you would be expected to be up to speed on, including algebra, statistics (including probability and regression), and calculus.
  • Artificial intelligence (AI): While some of the courses on this list will use computer vision as the entry point into learning about artificial intelligence, but most of them expect you to already have a grasp of the main concepts and how they are implemented. Check out our lists of recommended courses for artificial intelligence, machine learning, and deep learning. Our main recommendation for anyone looking to get into computer vision would be the AI Programming with Python Nanodegree as it is a very high-quality guided program which will also give you the solid foundation in Python that you will need if specializing in this field.

Exactly what is needed will of course depend on the specific course and a couple of the less programming-focused courses on this list have no prerequisites.

Recommended courses

Best Overall Course: Computer Vision Expert Nanodegree (Udacity x Affectiva & Nvidia)

Illustration of facial recognition
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Pros

  • High quality, practice-oriented curriculum designed together with industry leader
  • Excellent instructors
  • Includes dedicated guidance and support for learners

Cons

  • For intermediate to advanced programmers – not the best option if you do not have the prerequisite experience

With its 3-month curriculum focusing on teaching those with previous programming and machine learning experience, the Computer Vision Expert Nanodegree on Udacity is an intensive course that gives students expertise to become fully-fledged computer vision engineers. Designed together with Nvidia – an industry leader in computer vision – this course comes with Udacity’s dependably high-quality delivery, project-focused curriculum, mentoring, and career support services, and is our overall pick for anyone looking to start or upgrade their career in the area of computer vision.

Note that as this is an advanced topic, there are prerequisites, like for most courses on our list. Students are expected to have the following:

  • Intermediate to advanced Python experience.
  • Intermediate statistics background, including probability
  • Intermediate knowledge of machine learning techniques, including familiarity with backpropagation and examples of neural network architecture
  • Familiarity with a deep learning framework like TensorFlow, Keras, or PyTorch.

The program is divided into three component courses, each focusing on a portfolio-building project:

  1. Introduction to Computer Vision – focuses on the fundamentals of computer vision, but already at this stage applying deep learning techniques with a project on facial keypoint detection.
  2. Advanced Computer Vision and Deep Learning – takes learners through more advanced convoluted neural networks (CNNs) architecture, recurrent neural networks (RNNs), and attention mechanisms, and brings it all together in a project on image captioning combining CNNs and RNNs.
  3. Object Tracking and Localization – turns more to applications for mapping the environment with optical flow, feature matching, localization, and graph slam, to help you build a project on the localization techniques used in autonomous vehicle navigation.

The program has been around since 2018, but has been kept up-to-date on points where the technology has moved forward significantly.

As the course has a strong focus on AI applications of computer vision, it is not the best option for learners who want to start out with learning about the theoretical fundamentals of computer vision outside of AI. If this is important to you, we instead recommend first following the First Principles of Computer Vision Specialization from Coursera and Columbia University.

But if a career in computer vision is what you’re looking for, and you already have the necessary prerequisites, then this should be an easy choice. There are not any alternatives that we know of that will give you even nearly the degree of high-quality guidance and support in this particular field. Even if you don’t have everything yet, you may still want to aim to take this course after brushing up your skills with our recommendations for this in the intro. Your main option otherwise for a similarly high-quality offering is to go for a wider program in artificial intelligence, machine learning, or deep learning that also covers computer vision.

The cost of Udacity Nanodegrees, at $399/month or with a 15% discount if paying up front for the estimated duration of the program, is higher than most other more “self-service” style courses. But this reflects both the degree of work that has gone into the program design, and, more importantly, the support you are getting from teachers and assistants.

Best for Fundamentals: First Principles of Computer Vision Specialization (Coursera x Columbia University)

Screenshot of the course page for Coursera Specialization - Columbia University - First Principles of Computer Vision Specialization
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Pros

  • Comprehensive introduction to all "conventional" aspects of computer vision
  • Good starting point before continuing with applying AI to computer vision

Cons

  • Although listed as beginner-level, difficult without existing familiarity with computer vision

How does human vision work and how does it relate to how a digital camera functions? If you are interested in getting a solid understanding of the fundamentals of computer vision before learning more about how artificial intelligence can be applied to it, the First Principles of Computer Vision Specialization from Coursera and Columbia University is an excellent choice.

Note that while this course is categorized by Coursera as beginner level, if you have no prior familiarity with computer vision, you will likely struggle with the material, especially in the first course which moves at a rapid pace. We have changed the categorization to intermediate in our list, but as long as you are prepared to take extra time with the first course and pause frequently to research the topics mentioned, this course should be manageable as long as you have the prerequisites in math (fundamentals of linear algebra and calculus).

The course only touches on artificial intelligence applications at the end of the final course – looking at neural networks for object recognition. This makes for a great pivot point to our overall recommended course or another course on the list that focuses more on AI in computer vision.

Best Free Course: Introduction to Computer Vision (Udacity x Georgia Tech)

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Pros

  • Excellent coverage of fundamentals of computer vision
  • Although free, high quality course material and good instructors

Cons

  • No personalized support

Introduction to Computer Vision is a solid free course offering developed by two leading educational actors in this field: Udacity (which also provides our overall top pick in this list) together with Georgia Tech for self-driven learners with existing intermediate math and programming skills.

With the focus being on understanding and applying low and mid-level algorithms to analyze images, the course provides a great introduction to most of the traditional computer vision techniques. However, it does not cover machine learning to any greater extent.

Even though the subject matter is advanced, the course instructors present it clearly. Although not necessarily to everyone’s liking, they do try to keep things a bit more lively by injecting some humor into their delivery.

While it has a well-designed curriculum, this course is primarily designed for those studying on a budget – it does not have the same level of interactivity and support as Udacity’s Nanodegrees. This is a challenging, graduate-level course – if you would like to increase your chances of succeeding in this area, we strongly recommend Udacity’s full Nanodegree instead. On the other hand, it does not cost you anything but your time to test this one out.

Best High-Level Overview: Computer Vision - Image Understanding for Efficient Business and Industry (FutureLearn x Lulea University of Technology)

Digital representation of the iris of an eye
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Pros

  • Useful for programmers and non-programmers alike
  • Provides a higher-level perspective missing from the other courses on the list

Cons

  • Not very practically oriented

Are you interested in the applications of computer vision but you’re not a programmer? Computer vision is a very powerful tool and has the potential to transform several aspects of business – as such, it’s important for executives, managers, programmers in other fields, and many others to understand its possibilities and implications.

Designed for those interested in the topic without wanting to get straight into the programming implementation of it, the Computer Vision – Image Understanding for Efficient Business and Industry course from FutureLearn and Luleå University of Technology will take you through a high-level overview of the concepts and applications of computer vision. At an introductory level without any prerequisites, the four hours per week for three weeks is manageable for most.

Best Crash Course: Introduction to Deep Learning with OpenCV (LinkedIn Learning)

Picture of Jonathan Fernandes, course instructor for the LinkedIn Learning Course - Introduction to Deep Learning with OpenCV
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Pros

  • Quick overview of the main concepts to help orient yourself

It can be difficult to know where to start if you are not already familiar with computer vision. For those who already have some programming experience, the Introduction to Deep Learning with OpenCV from LinkedIn Learning is a good option for a first step. It provides a brief introduction to OpenCV and will give you a decent overview of some of the main frameworks and concepts, without getting into any major practical applications or projects.

As the course clocks in at only 49 minutes, this clearly will not let you master computer vision, but Jonathan Fernandes, the course instructor, does a fine job in helping you take your first few steps in building your understanding of the field.

Best Introductory Lab: Computer Vision 101 - Let's Build a Face Swapper in Python (Skillshare)

Picture of course instructor for Skillshare Course - Computer Vision 101: Let's Build a Face Swapper in Python
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Pros

  • Let's you get your hands dirty pretty much instantly

Cons

  • More inspirational than providing much in terms of lasting skills

Sometimes you just want to get your hands dirty without spending too much time learning about the theory behind something. If you already have experience with Python and want a quick practical introduction to what you can do with computer vision, then Computer Vision 101 – Let’s Build a Face Swapper in Python is for you. This practically oriented short course from Skillshare will quickly get you set up with OpenCV and have you running your own example project in no time. Good production value and delivery by Alvin Won.

Best Tool Coverage: Modern Computer Vision - PyTorch, Tensorflow2 Keras & OpenCV4 (Udemy)

Bounding boxes for object recognition applied to cars.
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Pros

  • Good option for getting an overview of the capabilities of multiple tools for those who prefer teaching via explained code snippets

Cons

  • Lacking contextual and theoretical explanations

There are several tools and libraries that are relevant for computer vision programmers. If you are a programmer looking to get introduced to some of these, we recommend the Modern Computer Vision course from Rajeev Ratan at Udemy. This course covers a wide range of tools with PyTorch, TensorFlow2 and OpenCV4 with a focus on explaining code snippets.

The course instructor is not the most experienced expert, and his explanations are lacking in explanations of overall principles and theory. However, he does a fine job introducing you to the practical applications of the tools covered and if you like learning via code snippet explanations, this course is a good option for you.

Best for Microsoft Azure: Applied Artificial Intelligence - Computer Vision and Image Analysis (FutureLearn x CloudSwyft)

Illustration of computer vision with hand and lines.
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Pros

  • Excellent course for those working in Microsoft Azure
  • Accredited by Microsoft

The Applied Artificial Intelligence – Computer Vision and Image Analysis  course from FutureLearn and CloudSwyft is an excellent introduction for computer vision using this platform, including both Microsoft tools and OpenCV. The course instructors take you through classical image analysis techniques before turning to deep learning object classification and applying the Microsoft ResNet Convolutional Neural Network (CNN) and the Microsoft Cognitive Toolkit.

The course is also accredited by Microsoft, helping ensure that you are getting the most relevant skills for working in computer vision using their tools. Note that the course is part of an ExpertTrack on AI in Microsoft Azure, and if you know you will be working on this platform, you would be well-advised to also take the other three related courses.

Best for MATLAB: Introduction to Data, Signal, and Image Analysis with MATLAB (Coursera x Vanderbilt University)

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Pros

  • Excellent course for those working in MATLAB
  • Clear instruction, if a little bit long-winded

If you’re one of the millions of engineers and scientists using MATLAB in your work, you may already be aware that this programming language is very capable in the area of computer vision. The Introduction to Data, Signal, and Image Analysis with MATLAB from Coursera and Vanderbilt University is a very good course introducing those who already have intermediate MATLAB knowledge to its data, signal, and image processing functionalities.

The course features useful assignments. The instructor, Jack Noble, has put a lot of effort into making sure the instructional videos are comprehensive and pedagogical – but they are a bit on the long side for some learners.

This course is part of the MATLAB Programming for Engineers and Scientists Specialization.

Best for Experts: Advanced Computer Vision with TensorFlow (Coursera x DeepLearning.AI)

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Pros

  • Excellent course for those working in TensorFlow
  • Great instructors
  • Curriculum focused on practical applications

Cons

  • Advanced level and high-paced without much support

TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today and is used for cutting-edge applications of computer vision. The practically oriented Advanced Computer Vision with TensorFlow from Coursera and Deeplearning.AI will help programmers deepen their knowledge of how to use this framework through a set of projects on object detection, image segmentation, and visualization and interpretability.

Designed for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow, it is not recommended as a standalone course for most students, but can be combined with the other advanced courses in the TensorFlow Advanced Techniques Specialization offered on Coursera. If you need an introduction to TensorFlow, the DeepLearning.AI TensorFlow Developer Professional Certificate is your best starting point.

Full list of recommended courses

PlatformTitleShort descriptionLinkPartnerLevel (starting)Length/DurationCostFree trialPrerequisitesCourse leads
UdacityComputer Vision Expert NanodegreeWith a 3-month curriculum designed with Nvidia together with Udacity's dependably high-quality delivery and mentoring and career support services, this Nanodegree is our overall pick for anyone looking to start or upgrade their career in the area of computer vision. Note that as this is an advanced topic, there are prerequisites.Read moreAffectiva & NvidiaIntermediate3 months (at 10-15 hrs/week)$399/month or $1,197 paid upfrontNoPython, statistics, machine learning, and deep learningSebastian Thrun; Cezanne Camacho; Alexis Cook; Juan Delgado, Jay Alammarl; Ortal Arel; Luis Serrano
UdacityIntroduction to Computer VisionA solid free course offering developed by Udacity together with Georgia Tech for self-driven learners with existing intermediate math and programming skills. Well-designed curriculum, but primarily designed for those studying on a budget – it does not have the same level of interactivity and support as Udacity's Nanodegrees.Read moreGeorgia TechIntermediate4 months (estimate)FreeN/AMath, Programming with Data structures, Matlab, NumpyAaron Bobick; Irfan Essa; Arpan Chakraborty
CourseraFirst Principles of Computer Vision SpecializationThis course from Columbia University has lower starting requirements, as it focuses on general computer vision principles rather than machine learning implementations. It serves as a good foundation for continuing on to a course on machine learning and computer vision.Read moreColumbia UniversityBeginner7 months (at 2 hours/week)$49/month or through Coursera Plus7-day free trialMath: fundamentals of linear algebra and calculus. No programming knowledge needed.Shree Nayar
CourseraComputer Vision BasicsA somewhat more condensed course on computer vision principles. A good overview for those who already have existing knowledge in the area, but most advanced topics are covered too superficially.Read moreUniversity at Buffalo SUNYIntermediate13 hours$49 or through Coursera PlusNoBasic programming skills & experience; familiarity with basic linear algebra, calculus & probability, and 3D co-ordinate systems & transformationsRadhakrishna Dasari; Junsong Yuan
CourseraIntroduction to Computer Vision and Image ProcessingFocusing on machine learning implementations of computer vision with Watson and IBM Cloud. Content often not detailed enough to actually understand the underlying operations and unfortunately labs are riddled with technical problems.Read moreIBMBeginner22 hours$49/month7-day free trialPython programming language; high school mathAije Egwaikhide; Joseph Santarcangelo
CourseraAdvanced Computer Vision with TensorFlowHigh quality curriculum with clear delivery from Deeplearning.AI on machine learning applications for computer vision. Not recommended as a standalone course for most students, as it becomes too challenging, but can be combined with the other advanced courses on TensorFlow on the same platform.Read moreDeepLearning.AIIntermediate29 hours$49/month7-day free trialMath: basic calculus, linear algebra, statistics; knowledge of AI, deep learning; Programming: Experience with Python, TF/Keras/PyTorch framework, decorator, context managerLaurence Moroney; Eddy Shyu
CourseraIntroduction to Data, Signal, and Image Analysis with MATLABA very good course introducing those who already have intermediate MATLAB knowledge to its data, signal, and image processing functionalities. Good assignments and high-quality instructional videos (albeit on the long side).Read moreVanderbilt UniversityIntermediate23 hours$49/month7-day free trialIntermediate MATLABJack Noble
CourseraComputer Vision with Embedded Machine LearningAn excellent course for those interested in machine learning embedded devices. Targeting those working on Raspberry Pi and using Edge Impulse and OpenMV, which narrows the scope but also the general applicability.Read moreEdge ImpulseIntermediate31 hours$49 or through Coursera Plus7-day free trialMath: reading plots, arithmetic, and algebra; Programming: PythonShawn Hymel
CourseraDeep Learning Applications for Computer VisionPart of the online Master of Science in Data Science, this course starts with looking at foundational computer vision principles and tools before moving to deep learning and CNNs. While comprehensive in scope, it does not deal with many of the topics at a deeper level.Read moreUniversity of Colorado BoulderIntermediate23 hours$49 or through Coursera PlusNoMath: Basic calculus, linear algebra; intermediate programmingIoana Fleming
CourseraComputer Vision Fundamentals with Google CloudA good introduction to using Google Cloud for Computer Vision projects. However, content is a bit out of date with instruction not mathching the labs and some labs are buggy.Read moreGoogle CloudAdvanced19 hours$39/month or through Coursera PlusYesDesigned for those already in the industryGoogle Cloud
CourseraComputer Vision in Microsoft AzureTargeting those already focusing on Microsoft Azure, the course does a good of introducing the capabilities of Microsoft's Computer Vision AI service. Part of preparatory program for AI-900 Microsoft Azure certification.Read moreMicrosoftBeginner8 hours$49/month or through Coursera Plus7-day free trialBasic computer literacyMicrosoft
UdemyModern Computer Vision™ PyTorch, Tensorflow2 Keras & OpenCV4This course covers a wide range of tools including PyTorch, TensorFlow2 and OpenCV4 with a focus on explaining code snippets. Lacking in explanations of overall principles and theory.Read moreBeginner27 hours 36 minutes (229 lectures)$99.9930-day money-back guaranteeBasic maths and Python programmingRajeev D. Ratan
UdemyPython for Computer Vision with OpenCV and Deep LearningThis course is a good option for those looking for a tutorial specifically on OpenCV and preferring code explanations as their mode of learning. The content is however somewhat out of date.Read moreBeginner14 hours 4 minutes (92 lectures)$94.9930-day money-back guaranteeBasic PythonJose Portilla
UdemyComputer Vision MasterclassGood as an introduction to libraries relevant for Computer Vision, but lacking in theoretical foundations and explaining why certain choices are made in the examples. Note that while the presenter has a good command of English, he has a fairly thick accent, in case this tends to interfere with your learning.Read moreBeginner25 hours 28 m$94.9930-day money-back guaranteeBasic Python programmingJones Granatyr; Ligency; Gabriel Alves; IA Expert Academy
UdemyDeep Learning and Computer Vision A-Z™: OpenCV, SSD & GANsWhile the content of this introductory course is of fairly high quality, it is unfortunately out of date, including for the development environment used in the examples with expired libraries and dependencies, making it very difficult to set up.Read moreBeginner11 hours 6 minutes (86 lectures)$94.9930-day money-back guaranteeBasic Python programming; High school mathsHadelin de Ponteves; Kirill Eremenko; Ligency
UdemyDeep Learning: Advanced Computer Vision (GANs, SSD, +More!)A good intermediate to advanced course on computer vision. It covers a variety of tools with fairly good explanations of the underlying concepts, but will be difficult to keep up without meeting the prerequisite familiarity of deep learning concepts and methods. Is regularly updated.Read moreIntermediate14 hours 59 minutes$99.9930-day money-back guaranteeIntermediate Python and Numpy; Basic concepts behind convolution and neural networks; building, training, and using a CNNLazy Programmer Inc.
UdemyConvolutional Neural Networks in Python: CNN Computer VisionGood introduction to CNNs with Keras and TensorFlow. Good code explanations and introductory sections, but with audio quality issues in recordings.Read moreBeginner7 hours 41 minutes (58 lectures)$19.9930-day money-back guaranteeNoneStart-Tech Academy
UdemyComputer Vision For iOS Developers CourseA decent introduction for Object Detection and Semantic Segmentation models for mobile and how to integrate them in an iOS app.Read moreAdvanced1 hour 52 minutesFreeN/AFor existing iOS developersAlexey Korotkov
UdemyLearn Computer Vision with OpenCV Library using PythonThe brief tutorial is more about introduction to OpenCV library and some sample programs than about computer vision in general but can be serve as a useful brief introduction.Read moreBeginner1 hour 11 minutesFreeN/ANoneFrederick Ngoiya
PluralsightGetting Started with OpenCV in .NETA good place to start if you are working in .NET. However, quite dated content and a quite superficial treatment of the topic.Read moreBeginner1 hour 27 minutesWith premium subscription: $38-$45/month10-day free trial.NETKobi Hikri
PluralsightComputer Vision Fundamentals with Google CloudA decent intermediate course on using Google Cloud for your computer vision projects, including good introductory explanations of concepts. High production value as with other courses by Google Cloud.Read moreIntermediate3 hours 7 minutesWith standard subscription: $25-$29/month10-day free trialGoogle CloudGoogle Cloud
PluralsightComputer Vision: Executive BriefingA short, high level briefing on core concepts of computer vision, useful for those looking for an inspiring overview of the topic.Read moreBeginner29 minutesWith standard subscription: $25-$29/month10-day free trialNoneXavier Morera
FutureLearnApplied Artificial Intelligence: Computer Vision and Image AnalysisPart of an ExpertTrack on AI on Microsoft Azure, this course is an excellent introduction for computer vision using this platform, including both Microsoft tools (including ResNet and Cognitive Toolkit) and OpenCV.Read moreCloudSwyftIntermediate4 weeks (at 5 hours/week)$39/month7-day free trialShould understand basics of image processing
FutureLearnComputer Vision: Image Understanding for Efficient Business and IndustryDesigned for those interested in the topic without wanting to get straight into the programming implementation of it, this course will take you through a high-level overview of the concepts and applications of computer vision.Read moreLuleå University of TechnologyBeginner (Introductory)3 weeks (at 4 hours/week)$69 or part of Unlimited7-day free trialNoneMarcus Liwicki; Hamam Mokayed
SkillshareComputer Vision 101: Let's Build a Face Swapper in PythonIf you want a quick practical introduction to what you can do with computer vision, this practically oriented short course will quickly get you set up with OpenCV and have you running your own example project. Good production value and delivery.Read moreIntermediate49 minutes$14-$32/month30-day free trialBasics of Python and AIAlvin Wan
LinkedIn LearningOpenCV for Python DevelopersA good, brief introduction to OpenCV, although the content is getting on the old side, especially for setting up a matching development environment.Read moreIntermediate2 hours 35 minutes$39.99 or part of subscription30-day free trialIntermediate PythonPatrick W. Crawford
LinkedIn LearningIntroduction to Deep Learning with OpenCVThis brief introduction to OpenCV will give you a decent overview of some of the main frameworks and concepts, without getting into any major practical projects.Read moreIntermediate49 minutes$29.99 or part of subscription30-day free trialIntermediate PythonJonathan Fernandes
EducativeImage Recognition with Machine LearningA good option for programmers wanting to add computer vision to their portfolio. Requires Python knowledge and being familiar with TensorFlow. The full Learning Path that this is part of can be a good alternative for those who need a wider introduction to machine learning.Read moreAdaptilabAdvanced7 hours (45 lessons)$16.66-$59/month7-day free trialPython and TensorFlowAdaptiLab
edXComputer Vision and Image Processing FundamentalsAn introductory course to computer vision, this course is particularly of interest to those using or interested in IBM's Watson. A variant of this course is also offered on Coursera.Read moreIBMBeginner3 weeks (at 3-4 hours/week)$99NoPython programming language; high school mathAije Egwaikhide; Joseph Santarcangelo
edXComputer Vision for Embedded SystemsA particularly good option for anyone working with embedded systems, this course focuses in on the constraints and resource requirements for computer vision on less powerful devices. Note that this is instructor-paced, so keep an eye out for when the next start date will be.Read morePurdue UniversityAdvanced5 weeks (at 7-8 hours/week)$750NoKnowledge of Python and Data ScienceYung-Hsiang Lu
edXRobotics: Vision Intelligence and Machine LearningFor those interested in robotics this is an excellent course for computer vision applications in this field. This course is currently only available as an archive – keep an eye out for the next date it is available.Read morePennAdvanced12 weeks (at 8-10 hours/week)$349NoMath: College-level introductory linear algebra and introductory calculus; Programming: Basic knowledgeJianbo Shi; Kostas Daniilidis; Dan Lee