Computer vision is used for face recognition, Optical Character Recognition, Object Recognition, 3D imaging, image-guided surgery, etc. So, If you are looking for Best Free Computer Vision Courses, this article is for you. In this article, you will find the 7 Best Free Computer Vision Courses.
Now without further ado, let’s get started-
Best Free Computer Vision Courses
1. Introduction to Computer Vision– Udacity
Time to Complete– 4 Months
This is a completely FREE Course to learn Computer Vision. In this free course, you will learn the fundamentals of image formation, camera imaging geometry, feature detection and matching, multiview geometry including stereo, motion estimation, tracking, and classification.
In this course, you will develop basic methods for applications that include finding known models in images, depth recovery from the stereo, camera calibration, image stabilization, automated alignment (e.g. panoramas), tracking, and action recognition.
This course will help you to develop the intuitions and mathematics of the methods and understand the difference between theory and practice in the problem sets.
You Should Enroll If-
- You have a good working knowledge of Matlab and/or Python with NumPy.
Interested to Enroll?
If yes, then check out the course details here- Introduction to Computer Vision
2. Computer Vision Basics– Coursera
Rating- 4.2/5
Time to Complete- 13 hours
This is a Free to Audit course on Coursera. That means you can access the course material free of cost but for the certificate, you have to pay.
In this course, you will understand the basics of computer vision and learn color, light, and image formation; early, mid-level, and high-level vision; and mathematics essential for computer vision.
You will apply mathematical techniques to complete computer vision tasks throughout this course. You will get a free license to install MATLAB for the duration of the course available from MathWorks.
You Should Enroll If-
- You have basic programming skills and are familiar with basic linear algebra, calculus & probability, and 3D coordinate systems & transformations.
Interested to Enroll?
If yes, then check out the course details here- Computer Vision Basics
3. Intel® Edge AI Fundamentals with OpenVINO™– Udacity
Time to Complete- 1 Month
This is another Free Course to understand the Intel® Distribution of OpenVINO™ Toolkit, which allows developers to deploy pre-trained deep learning models through a high-level C++ or Python inference engine API integrated with application logic.
Based on convolutional neural networks (CNN), the toolkit extends workloads across Intel® hardware (including accelerators) and maximizes performance.
In this course, you will leverage a pre-trained model for computer vision inferencing and convert pre-trained models into the framework-agnostic intermediate representation with the Model Optimizer.
After converting the pre-trained models, you will perform efficient inference on deep learning models through the hardware-agnostic Inference Engine and in the end, deploy an app on the edge.
You Should Enroll If-
- You have basic Python experience and basic familiarity with computer vision and AI model creation.
Interested to Enroll?
If yes, then check out the course details here- Intel® Edge AI Fundamentals with OpenVINO™
4. Advanced Computer Vision with TensorFlow– Coursera
Rating- 4.8/5
Time to Complete- 29 hours
This is a Free to Audit course. To access the course material for Free, press-> Enroll for Free and then press-> Audit the Course.
This course begins with a conceptual overview of image classification, object localization, object detection, and image segmentation. Then you will get an overview of some popular object detection models, such as regional-CNN and ResNet-50.
By using transfer learning, you will train a model to detect and localize rubber duckies using just five training examples.
After that, you will learn image segmentation using variations of the fully convolutional neural network. And you will build the fully convolutional neural network, U-Net, and Mask R-CNN to identify and detect numbers, pets, and even zombies!
At the end of this course, you will learn about the importance of model interpretability, which is the understanding of how your model arrives at its decisions.
You Should Enroll If-
- You have working experience with Python, TF/Keras/PyTorch framework, and basic knowledge of deep learning, calculus, linear algebra, and stats.
Interested to Enroll?
If yes, then check out the course details here- Advanced Computer Vision with TensorFlow
5. Computer Vision– Kaggle
Time to Complete- 4 hours
This Free Course is available on Kaggle. In this course, you will understand the fundamental ideas of computer vision and use modern deep-learning networks to build an image classifier with Keras.
You will also design your custom convnet with reusable blocks and learn the fundamental ideas behind visual feature extraction and Transfer learning.
You Should Enroll If-
- You have basic knowledge of Deep Learning.
Interested to Enroll?
If yes, then check out the course details here- Computer Vision
6. Introduction to Computer Vision and Image Processing– Coursera
Rating- 4.4/5
Time to Complete- 21 hours.
This is another Free to Audit Coursera course. To access the course material for Free, press-> Enroll for Free and then press-> Audit the Course.
In this course, you will learn the basics of image processing with Python libraries OpenCV and Pillow and the different Machine learning classification Methods commonly used for Computer vision, including k nearest neighbors, Logistic regression, SoftMax Regression, and Support Vector Machines.
Then you will learn about Neural Networks, fully connected Neural Networks, and Convolutional Neural networks (CNN). After that, you will understand object detection with different methods. The first approach is using the Haar Cascade classifier, the second one is to use R-CNN and MobileNet.
At the end of this course, you will build a computer vision app that you will deploy on the cloud through Code Engine. For the project, you will create a custom classifier, train it and test it on your images.
You Should Enroll If-
- You have some knowledge of the Python programming language and high school math.
Interested to Enroll?
If yes, then check out the course details here- Introduction to Computer Vision and Image Processing
7. Computer Vision with OpenCV Python | Official OpenCV Course– Udemy
Rating- 4.9/5
Time to Complete- 1hr 59min
This is a completely free course to learn OpenCV for Computer Vision. In this course, you will learn a wide range of Computer Vision topics like Image & Video Manipulation, Image Enhancement, Filtering, Edge Detection, Object Detection, and Tracking, Face Detection, and the OpenCV Deep Learning Module.
This course is created by the OpenCV Course Team. They are a team of AI practitioners and industry experts at OpenCV on a mission to educate a global workforce.
You Should Enroll If-
- You have basic knowledge of Python.
Interested to Enroll?
If yes, then check out the course details here- Computer Vision with OpenCV Python | Official OpenCV Course
And here the list ends. I hope these Best Free Computer Vision Courses will help you to learn Computer Vision. I would suggest you bookmark this article for future referrals. Now it’s time to wrap up.
Conclusion
In this article, I tried to cover all the Best Free Computer Vision Courses. If you have any doubts or questions, feel free to ask me in the comment section.
All the Best!
Enjoy Learning!
NOTE- Some of the links in the post are Affiliate Links. This means if you click on the link and purchase the course, I will receive an affiliate commission at no extra cost to you😊.
