13 Best Computer Vision Courses, Certifications & Training Programs Online In 2023
Embark on a fulfilling journey to master computer vision with our comprehensive list of the top 13 online courses, certifications, and training programs, covering everything from fundamentals to advanced applications, and catering to learners of all skill levels in this rapidly growing field.
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Embarking on a journey to master computer vision in today's technologically driven world? Look no further! After thoroughly researching 179 popular computer vision courses from various providers, with a staggering 2,754,620 enrolled students who left over 228,628 ratings and reviews, we have carefully selected the top 13 courses that cater to learners from all skill levels. Our evaluation process included analyzing ratings, reviews, enrollment numbers, learner experiences, content quality, curriculum comprehensiveness, release dates, and affordability, all combined with our expertise in the field.
Divided into categories such as practical digital image and video processing, mastering convolutional neural networks, understanding computer vision basics, deep learning in face recognition, robotics vision intelligence, and many more, these courses offer a wide range of knowledge to suit your specific interests and learning goals. Don't miss the opportunity to learn from the best in computer vision and advance your professional career with these carefully curated courses!
This course delves into the fundamental principles and tools utilized in processing digital images and videos, and demonstrates how to apply them in solving practical problems across commercial and scientific fields. Digital images and videos are prevalent in a myriad of applications, spanning a wide range of the electromagnetic spectrum - from visible light, infrared, gamma rays, and beyond. Thus, the ability to process image and video signals is an essential skill for engineering and science students, software developers, and practicing scientists, as digital image and video processing continues to drive the multimedia technology revolution we are experiencing today.
The course covers the essentials of image and video processing, providing a mathematical framework to describe and analyze images and videos as two- and three-dimensional signals in the spatial, spatio-temporal, and frequency domains. Students will not only learn the theory behind key processing tasks such as image and video enhancement, recovery, and compression, but also how to execute these tasks using state-of-the-art techniques and tools. A range of optimization toolboxes and statistical techniques will be introduced, with an emphasis on the unique role of sparsity in modern image and video processing. Throughout the course, specific application domain examples of images and videos will be utilized to ensure practical understanding.
User review:
As an amateur photographer who is interested in post-processing, I came here to find more about how image processing softwares work. Sometimes it took me lots of time to catch up what the professor was teaching. This course is not friendly to the person who does not have basic knowledge about signal processing and math. And the professor's accent is quite noticeable to me, a non-native English speaker, plus there are tons of errors and [UNKNOWN] in the subtitles, which is the one biggest challenges I had met. But frankly speaking, This course is great in the most of aspects, I have learnt a lot from it. The most of tests are relative easy compared to the lectures.
BTW, In the final MATLAB test, there is a hint about normc function, that is useless for the student who is using MATLAB online because the function belongs to additional toolkits that online users will not have. [1]... Read More
昊 黄
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This course excels in providing the foundational principles and tools essential for understanding and executing processing tasks in digital images and videos, preparing students to solve practical problems across various commercial and scientific fields.
This course offers a comprehensive introduction to Convolutional Neural Networks (CNN) in Python, covering essential concepts and techniques for building image recognition models. It caters to students, analysts, and machine learning scientists who aspire to learn and apply deep learning concepts in real-world image recognition problems. The course covers both basic and advanced CNN models such as LeNet, GoogleNet, and VGG16, and teaches you how to create CNN models in Python using Keras and TensorFlow libraries.
With a strong emphasis on understanding the theoretical concepts behind deep learning and their implementation, the course follows a step-by-step process to create an image recognition model using Convolutional Neural Networks. Upon completion of the course, students will have a firm grasp of creating and implementing CNN models and will be able to confidently apply the knowledge gained in solving complex image recognition challenges. Expect improvements in accuracy through techniques such as Data Augmentation and Transfer Learning, taking you to the cutting edge of deep learning in the field of computer vision.
User review:
I realized that I had missed many things when I tried to learn from the other source before. Without proper understanding, I couldn't try much with the dataset but when I went through this course I had much clarification. I understood the core of how it actually works behind the code which I was looking everywhere. I believe now I will be able to make a well-trained model and fulfill my pending ideas. The instructors on this course were precise and could make understand in simple words. There were able to clear doubts for asked queries. As per my experience, they should be highly skilled in this stack. This is also my first course on Udemy. I would like to thank Udemy as well as Start-Tech academy members for supporting and sharing knowledge among us. [2]... Read More
Bikram Pandit
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This course is perfect for individuals who aspire to gain expertise in deep learning concepts and techniques related to Convolutional Neural Networks for image recognition. Acquire proficiency in creating and implementing CNN models using Keras and TensorFlow libraries in Python.
This course provides a comprehensive introduction to the field of computer vision, aiming to equip learners with an understanding of its mission to enable computers to see and interpret the world as humans do. By learning about the core concepts and key application areas of computer vision, participants will gain an appreciation for the digital imaging process and the synergy between digital signal processing, neuroscience, and artificial intelligence. In addition, this course will cover topics such as color, light, image formation and the different levels of vision processing, as well as the essential mathematics required for computer vision tasks.
Designed for individuals who are curious about the concepts of computer vision or those in need of a refresher course on its mathematical aspects, this course assumes that learners have a basic grasp of programming skills, particularly in MATLAB. In order to fully benefit from the course material, which includes online lectures, videos, demos, hands-on exercises, project work and discussions, participants should be familiar with basic linear algebra, 3D coordinate systems, calculus, and probability. Throughout the course, learners will have opportunities to apply their knowledge by writing computer vision programs using MATLAB and supporting toolboxes, with a free MATLAB license available from MathWorks for the course duration.
User review:
The instructor gives pretty good in explaining things however the matlab assignment is frustrating after several attempts failure. More guidance probably should be given for the matlab assignment or it get really frustrating after 6 hrs stuck at the same position struggling to guess the real answer. You should at least familiar with matlab operation for getting started in this one. "intermediate level" is pretty accurate. I am a undergrad year-2 EE student at a Top 10 UK uni and this still remains a bit challenging. The overall level is OK but sometimes stuck at a same place for hours really make me wants to give up for some time.
But overall its a really good course but probably for for a total beginner. [3]... Read More
Mao S
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This course is ideal for those seeking to gain a comprehensive understanding of the field of computer vision, its core concepts, and key application areas, as well as an appreciation of the synergy between digital signal processing, neuroscience, and artificial intelligence.
This course offers a comprehensive introduction to the development of a face recognition system by utilizing the power of deep learning. With growing applications of face recognition in various fields such as automated image tagging and mobile device authentication, advancements in deep learning have significantly improved the accuracy of face recognition systems. Through this course, you will gain insights into setting up a proper development environment, as well as exploring essential tools and techniques for effective face recognition. The course covers topics such as training a machine learning model for facial landmark analysis, coding facial feature detection, and face encoding, among others.
In addition to the fundamentals, the course delves into more advanced applications, including the creation of "digital makeup," a popular feature in modern mobile apps. Learners will also be equipped with the knowledge to repurpose and adjust pre-existing systems to suit various requirements. Furthermore, the course places a strong emphasis on hands-on learning, ensuring that each concept is reinforced by practical, real-world examples aimed at enhancing the learner's overall understanding of computer vision and the importance of deep learning in modern face recognition systems. By the end of this course, you will have a strong foundation in face recognition technology and be well-prepared to develop your own sophisticated face recognition applications.
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This course provides a comprehensive introduction to developing a face recognition system utilizing deep learning, with a focus on accuracy improvements and advanced applications such as digital makeup.
Deep learning has revolutionized computer vision, enabling the development of systems that can recognize objects in photographs with remarkable accuracy. This course provides an in-depth understanding of how to design, build, and deploy a deep neural network for image recognition. Gain insights into fine-tuning state-of-the-art deep neural networks to recognize new objects without the need for retraining, as well as exploring cloud-based image recognition APIs that serve as an alternative to building your own systems.
Throughout the course, you will learn the steps involved in building and deploying your own image recognition system, gaining valuable skills in deep learning, neural networks, and computer vision. By the end of the course, you will have a strong foundation in these areas, enabling you to design innovative image recognition solutions that can be applied in various scenarios, from searching photo libraries to generating text-based descriptions of photographs. Equip yourself with the knowledge and expertise vital for success in the rapidly evolving field of deep learning and computer vision.
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This course is ideal for individuals seeking to gain expertise in designing, building, and deploying deep neural networks for image recognition, while acquiring valuable skills in deep learning, neural networks, and computer vision.
This comprehensive course in deep learning and computer vision offers a unique opportunity for you to acquire the knowledge and skills needed to become a creator in this rapidly growing field. Focusing on the application of OpenCV, SSD, and GANs, the course will guide you through the maze of tools, libraries, and methods that are essential to understanding how to utilize computer vision in various industries, such as healthcare, retail, and entertainment. With its emphasis on practical applications, you'll learn not only how the most popular computer vision methods work but also how to apply them effectively in real-world situations.
Computer vision has numerous applications in today's world, and its importance cannot be overstated. From tumor detection in patient MRI brain scans to the identification of new business opportunities, mastering computer vision can lead to significant benefits for both individuals and organizations. By enrolling in this course, you'll gain the expertise you need to navigate the world of computer vision successfully and unlock its vast potential. Prepare to dive deep into the world of artificial intelligence and create powerful applications that have the ability to make a real difference in people's lives.
User review:
There's a lot to learn on subjects as deep learning and computer vision. I like that all the concepts are explained in a way that is understandable and that they always give us links to papers and books in case we want to dive deeper in everything we are learning. This is very necessary as there is always something that needs more understandings because the concepts are difficult. I think that Jordan is also making a very good job with his answers on the students questions. He tries to answer the question and also gives us links for a deeper understanding of what we are learning.
On the other side, it would be excellent to add true subtitles not automatically translated by Google because in various videos the audio quality / pronunciation of the teacher is not the best and therefore the subtitles are not precise and I've found myself repeating many times a phrase of the video because I didn't really understand, and sometimes I couldn't decipher at all. [4]... Read More
Gerard M
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This course is ideal for those who want to gain proficiency in digital image and video processing using OpenCV, single shot detector (SSD), and generative adversarial networks (GANs) in the context of deep learning and computer vision.
Computer Vision is one of the most exciting fields in Machine Learning and AI. It has applications in many industries, such as self-driving cars, robotics, augmented reality, and much more. In this beginner-friendly course, you will understand computer vision and learn about its various applications across many industries.
As part of this course, you will utilize Python, Pillow, and OpenCV for basic image processing and perform image classification and object detection. This is a hands-on course and involves several labs and exercises. Labs will combine Jupyter Labs and Computer Vision Learning Studio (CV Studio), a free learning tool for computer vision. CV Studio allows you to upload, train, and test your own custom image classifier and detection models. At the end of the course, you will create your own computer vision web app and deploy it to the Cloud. This course does not require any prior Machine Learning or Computer Vision experience. However, some knowledge of the Python programming language and high school math is necessary.
User review:
Very good course. The last part of publishing the classifier to the cloud could not work for me. But it was optional for the grading, so was not an issue. I’d have liked it to work though.
This course really opens up your horizons to the world of computer vision. So I’d highly recommend it to you even if you’re not doing the IBM certification as it introduces to a very interesting open source library called OpenCV.
Some (marketing style) material regarding IBM is expected. But I felt it was a bit of an overkill (especially In the first week). Fortunately all down to studying thereafter! [5]... Read More
Rohit B
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This course is best for providing a beginner-friendly introduction to computer vision, covering basic image processing and various applications across industries using Python, Pillow, and OpenCV.
This advanced course, offered by the University of Toronto, focuses on visual perception for self-driving cars and is ideal for learners with a background in computer vision and deep learning. The course covers the main perception tasks in autonomous driving, static and dynamic object detection, and provides a comprehensive overview of common computer vision methods for robotic perception. Participants can expect to gain hands-on experience with the pinhole camera model, intrinsic and extrinsic camera calibration, image feature detection, description, and matching, as well as designing their own convolutional neural networks.
Throughout the course, learners will apply their newly acquired skills to various aspects of self-driving car perception systems, such as visual odometry, object detection and tracking, and semantic segmentation for drivable surface estimation. The final project will involve developing algorithms that identify bounding boxes for objects in a scene and defining the boundaries of the drivable surface. Working with both synthetic and real image data, course participants will also have the opportunity to evaluate their performance on a realistic dataset. A Python programming background and familiarity with linear algebra concepts are required for successful completion of this course.
User review:
I really liked the online course. I found it well planned and designed, easy for me to follow, the workload was sufficient, so I was able to finish everything with enough time, learn about the topics and not feel overloaded and rushed. The course is just as mentioned, fun but with a lot of work, and it was! I enjoyed the web labs because they were fun and easy to understand. And the answers were usually somewhere in the text. I also liked the assignments that required us to be online. Sometimes the articles, in addition to the reading book, gave me a better idea and understanding of that week's topic. I will say that I learned quite a bit in this course, I enjoyed it as well..... So that's saying something. The assignments were very beneficial to the whole learning process. the instructors Steven Waslander and Jonathan Kelly make the concepts easy to understand, they are very good teachers. [6]... Read More
Jose d J E L
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This course is best for learners looking to focus on visual perception in self-driving cars, covering main perception tasks in autonomous driving and common computer vision methods for robotic perception.
This comprehensive course in robotics delves into the captivating realm of visual intelligence and machine learning, allowing participants to learn how to design robot vision systems that can effectively avoid collisions, safely work in tandem with humans, and accurately comprehend their surroundings. Taking a deep dive into robots’ abilities to “sense” and “recognize” the environment they operate in, the course also explores how robots can “learn” from past experiences by uncovering patterns in visual signals using artificial intelligence (AI).
Throughout the course, students will gain a solid understanding of how machine learning is used to extract statistically meaningful patterns from data, enabling classification, regression, and clustering. Investigating both computer vision and machine learning, participants will be equipped with the skills to build recognition algorithms that can adapt to new environments through learning from data. By completion, students will have the ability to program vision capabilities like robot localization and object recognition using machine learning. Engaging projects using MATLAB and OpenCV will cover real-life applications such as video stabilization, 3D object recognition, coding classifiers, building perceptrons, and designing convolutional neural networks (CNNs).
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This course excels in exploring the captivating realm of visual intelligence and machine learning in the context of robotics, enabling learners to design robot vision systems for effective collision avoidance, safe human interaction, and accurate environmental comprehension.
This comprehensive course offers an introduction to the fundamental concepts of the Intel Distribution of OpenVINO toolkit, which plays a significant role in the development of computer vision applications. Covering a range of informative demonstrations and examples, it enables learners to grasp the value of tools and utilities provided within the toolkit, such as the model downloader, model optimizer, and inference engine. While the course is designed for individuals with no prior experience in computer vision, previous knowledge in this field can be advantageous.
Specifically tailored to meet the needs and interests of anyone intrigued by core concepts of computer vision applications and the Intel Distribution of OpenVINO toolkit, this course requires an estimated workload of around 3 hours for completion. Regardless of an individual's existing understanding of computer vision, the course is structured to cater to all levels and abilities, providing a comprehensive and accessible learning experience for every participant involved.
User review:
This course is much helpful for those who want to understand the applications of Deep learning because Intel also provides Deep Learning Development Toolkit(DLDT) which can be use for model inference for various applications like surveillance, traffic monitoring specially in smart cities and also in industries. So it was an excellent course to learn something new about deep learning. [7]... Read More
Keshab K
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This course is ideal for those seeking an introduction to the fundamental concepts of the Intel Distribution of OpenVINO toolkit and its role in developing computer vision applications.
Deep learning, a groundbreaking branch of artificial intelligence, has transformed the way we analyze and interpret patterns in data, including visual information found in images and videos. By employing advanced image recognition capabilities, deep learning models with multiple layers and abstractions can exhibit almost human-like abilities. This course focuses on leveraging OpenCV, a widely used computer vision library, to run pre-trained deep learning models on cost-effective hardware and extract valuable insights from digital images and video content. Instructor Jonathan Fernandes introduces you to the fascinating world of deep learning via inference, using the OpenCV Deep Neural Networks (dnn) module.
Throughout the course, you will gain a comprehensive understanding of deep learning concepts and architecture. With the help of OpenCV and Python, you will learn how to view and load images and videos to apply these concepts practically. In addition, Jonathan demonstrates how to provide classification for both images and videos, use blobs (which are equivalents of tensors in other frameworks), and utilize YOLOv3 for custom object detection. By the end of this course, you will have a solid grasp of computer vision and deep learning techniques, allowing you to harness the power of AI for various image and video processing applications.
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This course is ideal for those who want to utilize OpenCV for deep learning applications, including image and video classification, custom object detection using YOLOv3, and understanding deep learning concepts and architecture.
Discover the world of advanced computer vision techniques with the help of TensorFlow in this comprehensive course. Gain expertise in various aspects of computer vision, including image classification, image segmentation, object localization, and object detection. Learn how to apply transfer learning to enhance object localization and detection while working with state-of-the-art models such as regional-CNN and ResNet-50. Develop skills to customize existing models and build your own for detecting, localizing, and labeling distinct images such as rubber ducks, numbers, pets, and even zombies.
Additionally, dive deep into the implementation of image segmentation using different variations of fully convolutional networks (FCNs), including U-Net and Mask-RCNN. Understand how class activation maps and saliency maps can be employed to identify which parts of an image are utilized by the model to make its predictions. Utilize these machine learning interpretation methods to inspect and improve the design of renowned networks like AlexNet. Designed for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow, this course facilitates the learning of advanced TensorFlow features and equips learners with the knowledge to build powerful models in the realm of computer vision.
User review:
I thought this was the best course in the tensorflow series so far! You get to learn about more sophisticated architectures like FCN, U-Net, ResNet, etc. The programming exercises take a little more time than the other courses and are intended to help you load models and restore checkpoints from new models you find on blogs. It would be great to also have classes on NLP and reinforcement learning at this level. [8]... Read More
Eric L
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This course is the best for learning advanced computer vision techniques using TensorFlow, including image classification, segmentation, object localization, and detection. It also covers transfer learning, customization of existing models, and implementation of image segmentation using fully convolutional networks.
This comprehensive course dives deep into advanced computer vision, covering topics such as transfer learning, TensorFlow object detection, classification, and Yolo object detection. Through hands-on experience with real-time projects and using free GPU provided by Google, you will learn how to set up TensorFlow object detection API, progress to modern neural architectures like ResNet and Inception, and eventually achieve object detection using both TensorFlow and YOLO algorithms.
Throughout the course, you will also gain practical experience training models in Google Colab and expand your understanding of the core basics of convolutional neural networks (CNNs). Through high-level building blocks, the course eliminates the complexity of low-level coding, with most of the material presented in Keras. With this course, you will expand your knowledge of computer vision and deep learning, applying it to object detection algorithms like SSD and YOLO, neural style transfer, and utilizing transfer learning techniques to build state-of-the-art computer vision models.
User review:
Love this course. Instructor is very knowledgeable and teaches very clearly. It was my first ever computer vision course. I had no idea about basics of OpenCV or any other tool used in the project. He taught everything so patiently, function by function that I have planned to change my domain of work. I'm looking forward for his other courses and trying to master this domain.
Once again, Thanks for this lovely tutorial, a hand holding guide to get one started in computer vision. Absolutely recommended course!. [9]... Read More
Jeremy
Best for:
This course excels at providing knowledge and proficiency in advanced computer vision techniques, focusing on transfer learning, TensorFlow object detection, classification, and Yolo object detection while offering hands-on, real-time projects and free GPU access.
Choosing the best computer vision course can be a daunting task, given the number of available options. To help you make the right choice, we have compiled a list of essential factors to consider while selecting a course that caters to your unique interests and goals. Regardless of the specific area of computer vision you seek to dive into, the following points will help guide you in making an informed decision.
Ensure that the course matches your desired learning outcomes. Depending on your intent, you may want to focus on digital image and video processing, convolutional neural networks for computer vision, or deep learning applications such as face and image recognition.
Examine the course's scope and depth. Beginners should opt for an introductory course on computer vision and image processing, whereas those with a basic understanding may prefer exploring specialized topics, including visual perception for self-driving cars or vision intelligence for robotics.
Take note of which technologies and tools the course covers. For instance, OpenCV, SSD, GANs, and the Intel® Distribution of OpenVINO™ toolkit can significantly augment your computer vision skillset.
Ascertain the quality of the course instructors and their expertise in the field. Ideally, the course should be taught by professionals with experience in both academia and real-world applications of computer vision techniques.
Read reviews from past students to gain insights into the course content, instructors, and overall experience. Positive feedback is a good indicator of the course’s effectiveness in helping learners achieve desired outcomes.
Consider your learning style and preferences. Opt for a course that provides a mix of theoretical explanations, practical exercises, and projects to keep you engaged and motivated throughout the learning process.
By taking these factors into account, you can confidently select a computer vision course that meets your needs and helps you stay at the forefront of this rapidly evolving field.
Conclusion
As you embark on your journey to master computer vision, these curated courses will provide a comprehensive understanding of digital image and video processing, deep learning techniques, and applications in domains such as robotics and self-driving cars. Whether you're a beginner seeking an introduction to the basics or an experienced professional looking to further specialize in advanced topics, there is a course tailored to fit your learning needs.
Through dedication and commitment, you will not only gain the proficiency needed to tackle challenges in computer vision, but also equip yourself with the invaluable skills required to stay at the forefront of this rapidly growing field. Computer vision has become an integral part of many industries and by investing time and effort into these courses, you will be taking a monumental step towards your future success. Don't wait any longer, seize the opportunity and kickstart your computer vision learning journey today!
How much does a computer vision course cost?
The cost of a computer vision course varies depending on the platform and the type of access you prefer. Generally, prices range from free to $349. Some platforms offer subscription-based pricing with free trials or monthly and annual plans, while others have one-time fees.
How long do computer vision courses take?
The duration of computer vision courses varies depending on the specific course and its content. Course durations can range from less than an hour to over 150 hours. It's important to consider the time commitment required for each course before enrolling.
What factors should I consider when choosing a computer vision course?
When choosing a computer vision course, consider factors such as cost, duration, course content, the level of complexity, and the instructor's reputation. Additionally, make sure the course aligns with your learning objectives and your prior experience in the field.
