Here is the list of 5+ Best Computer Vision Courses to know in 2022. We have finalized and listed these Best Computer Vision Courses based on the concepts, Learner reviews and the Total number of candidates who took these courses. I think these Best courses to learn Computer Vision in 2022 will really helpful to you.
Best Computer Vision Courses to Know in 2022
The list of Computer Vision Courses is presented in an order based on the help of the readers. Please go through them and enjoy reading.
First Principles of Computer Vision Specialization
Rating: 4.8
Provider: Columbia University
This specialization Course presents the first comprehensive treatment of the foundations of computer vision. It focuses on the mathematical and physical underpinnings of vision and has been designed for learners, practitioners and researchers who have little or no knowledge of computer vision.
The program includes a series of 5 courses. Any learner who completes this specialization has the potential to build a successful career in computer vision, a thriving field that is expected to increase in importance in the coming decades.
You will develop the fundamental knowledge of computer vision by applying the models and tools including: image processing, image features, constructing 3D scene, image segmentation and object recognition.
The specialization includes roughly 250 assessment questions. Proficiency in the fundamentals of computer vision is valued by a wide range of technology companies and research organizations.
Skills you will gain:
- Perception
- Object Recognition
- Camera and Imaging
- 3D reconstruction
- Fourier Transforms
- High Dynamic range Imaging
- Image Formation
- Convolution and Deconvolution
- Scale Space
- Active Contours
Coursesr / Topics:
- Camera and Imaging
- Features and Boundaries
- 3D Reconstruction and Single View Point
- 3D Reconstruction and Multiple Viewpoints
- Visual Perception
View this Course on Coursera
Advanced Computer Vision with TensorFlow
Rating: 4.8/5
Provider: Deeplearning.ai
In this course, you will:
a) Explore image classification, image segmentation, object localization, and object detection. Apply transfer learning to object localization and detection.
b) Apply object detection models such as regional-CNN and ResNet-50, customize existing models, and build your own models to detect, localize, and label your own rubber duck images.
c) Implement image segmentation using variations of the fully convolutional network (FCN) including U-Net and d) Mask-RCNN to identify and detect numbers, pets, zombies, and more.
d) Identify which parts of an image are being used by your model to make its predictions using class activation maps and saliency maps and apply these ML interpretation methods to inspect and improve the design of a famous network, AlexNet.
Syllabus:
- Introduction to Computer Vision
- Object Detection
- Image Segmentation
- Visualization and Interpretability
View this Course on Coursera
Introduction to Computer Vision and Image Processing
Rating: 4.4
Provider: IBM
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.
Skills you will gain:
- Deep Learning
- OpenCV
- AI
- Image Processing
- Computer Vision
Topics:
- Introduction to Computer Vision
- Image Processing with OpenCV and Pillow
- Machine learning image Classification
- Neural Networks and Deep Learning for Image Classification
- Object Detection
- Project: Not Quite a self driving car: Traffic Sign Classification
View this Course on Coursera
Become a Computer Vision Expert:
Provider: Udacity
Time duration: 3 months
If you’re new to Computer Vision, and eager to explore applications like facial recognition and object tracking, the Computer Vision Nanodegree program is an ideal choice.
The curriculum introduces you to image analysis with Python and OpenCV, then goes on to cover deep learning techniques that can be applied to a variety of image classification and regression tasks.
Over the course of the program, you’ll leverage your Python coding experience to build a broad portfolio of applications that showcase your newly-acquired Computer Vision skills.
Learn cutting-edge computer vision and deep learning techniques—from basic image processing, to building and customizing convolutional neural networks.
Apply these concepts to vision tasks such as automatic image captioning and object tracking, and build a robust portfolio of computer vision projects.
View this Course on Udacity
Modern Computer Vision PyTorch, Tensorflow2 Keras & OpenCV4
In this course, you will learn the essential very foundations of Computer Vision, Classical Computer Vision (using OpenCV) I then move on to Deep Learning where we build our foundational knowledge of CNNs and learn all about the following topics:
What you will learn:
- All major Computer Vision theory and concepts!
- Learn to use PyTorch, TensorFlow 2.0 and Keras for Computer Vision Deep Learning tasks
- OpenCV4 in detail, covering all major concepts with lots of example code
- Learn all major Object Detection Frameworks from YOLOv5, to R-CNNs, Detectron2, SSDs, EfficientDetect and more!
- Deep Segmentation with U-Net, SegNet and DeepLabV3
- Generative Adverserial Networks (GANs) & Autoencoders – Generate Digits, Anime Characters, Transform Styles and implement Super Resolution
- Facial Recognition along with Gender, Age, Emotion and Ethnicity Detection
- Important Modern CNNs designs like ResNets, InceptionV3, DenseNet, MobileNet, EffiicentNet and much more!
- And many more
Who this course is for:
- College/University Students of all levels Undergrads to PhDs (very helpful for those doing projects)
- Software Developers and Engineers looking to transition into Computer Vision
- Start up founders lookng to learn how to implement thier big idea
- Hobbyist and even high schoolers looking to get started in Computer Vision
Requirements
- No programming experience (some Python would be beneficial)
- Basic highschool mathematics
View this Course on Udemy
Deep Learning: Advanced Computer Vision (GANs, SSD, +More!):
In this course, you’ll see how we can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label.
You can imagine that such a task is a basic prerequisite for self-driving vehicles. (It must be able to detect cars, pedestrians, bicycles, traffic lights, etc. in real-time)
We’ll be looking at a state-of-the-art algorithm called SSD which is both faster and more accurate than its predecessors.
Another very popular computer vision task that makes use of CNNs is called neural style transfer.
This is where you take one image called the content image, and another image called the style image, and you combine these to make an entirely new image, that is as if you hired a painter to paint the content of the first image with the style of the other. Unlike a human painter, this can be done in a matter of seconds.
I will also introduce you to the now-famous GAN architecture (Generative Adversarial Networks), where you will learn some of the technology behind how neural networks are used to generate state-of-the-art, photo-realistic images.
Currently, we also implement object localization, which is an essential first step toward implementing a full object detection system.
Who this course is for:
- Students and professionals who want to take their knowledge of computer vision and deep learning to the next level
- Anyone who wants to learn about object detection algorithms like SSD and YOLO
- Anyone who wants to learn how to write code for neural style transfer
- Anyone who wants to use transfer learning
- Anyone who wants to shorten training time and build state-of-the-art computer vision nets fast
View this Course on Udemy
Master Computer Vision OpenCV4 in Python with Deep Learning:
In this course, you will discover the power of OpenCV in Python, and obtain skills to dramatically increase your career prospects as a Computer Vision developer.
I take a very practical approach, using more than 50 Code Examples.
At the end of the course, you will be able to build 12 Awesome Computer Vision Apps using OpenCV in Python.
What you will learn:
- Understand and use OpenCV4 in Python
- How to use Deep Learning using Keras & TensorFlow in Python
- Create Face Detectors & Recognizers and create your own advanced face swaps using DLIB
- Create Augmented Reality Apps
- Programming skills such as basic Python and Numpy
- How to use Computer Vision in executing cool startup ideas
- Understand Neural and Convolutional Neural Networks
- Learn to Perform Neural Style Transfer Using OpenCV
- Learn how to do Multi Object Detection in OpenCV (up to 90 Objects!) using SSDs (Single Shot Detector)
Who this course is for:
- Beginners who have an interest in computer vision
- College students looking to get a head start before starting computer vision research
- Anyone curious using Deep Learning for Computer Vision
- Entrepreneurs looking to implement computer vision startup ideas
- Hobbyists wanting to make a cool computer vision prototype
- Software Developers and Engineers wanting to develop a computer vision skillset
View this Course on Udemy
Conclusion:
The list of 5+ Best Computer Vision Courses that we have finalized is useful to everyone. If you found any of the Best Computer Vision Courses are missing then feel free to comment in the comment section.
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