The Computer Vision specialization takes you from the foundations of computer vision to the cutting edge of multimodal AI. Whether you're just starting out or looking to deepen your expertise, you'll gain the skills to build intelligent systems that interpret and generate visual data—just like today’s most advanced AI models.
Computer Vision Specialization
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Computer Vision Specialization
Computer Vision from Fundamentals to Advanced. Learn how machines interpret the visual world.
Instructor: Tom Yeh
Included with 
Get in-depth knowledge of a subject
4.0
(5 reviews)
Intermediate level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
4.0
(5 reviews)
Intermediate level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
Build a strong foundation in how machines perceive and analyze visual information.
Train deep learning systems for tasks such as image classification and segmentation.
Discover how transformers, Vision Transformers (ViT), CLIP, and diffusion models are reshaping the future of AI.
Explore "by hand" the core principles of image processing, feature extraction, and classical vision techniques.
Overview
Skills you'll gain
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
Recently updated!
August 2025
65 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
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Specialization - 3 course series
What you'll learn
Understand the fundamental principles and algorithms of classical computer vision.
Apply deep learning models to various computer vision tasks.
Evaluate and implement computer vision solutions for real-world applications.
Skills you'll gain
Category: Computer Vision
Category: Image Analysis
Category: Linear Algebra
Category: Computer Graphics
Category: Computational Thinking
Category: Probability Distribution
Category: Data Ethics
Category: Deep Learning
Category: Artificial Intelligence
Category: Data Processing
What you'll learn
Improve model performance and training stability using multilayer perceptrons (MLPs) and applying normalization techniques.
Implement autoencoders for unsupervised feature learning and design Generative Adversarial Networks (GANs) to generate synthetic images.
Train convolutional neural networks (CNNs) for image classification tasks, understanding how layers extract spatial features from visual data.
Apply advanced architectures like ResNet for deep image recognition and U-Net for image segmentation.
Skills you'll gain
Category: Artificial Intelligence
Category: Unsupervised Learning
What you'll learn
Apply Nonlinear Support Vector Machines (NSVMs) and Fourier transforms to analyze and process visual data.
Use probabilistic reasoning and implement Recurrent Neural Networks (RNNs) to model temporal sequences and contextual dependencies in visual data.
Explain the principles of transformer architectures and how Vision Transformers (ViT) perform image classification and visual understanding tasks.
Implement CLIP for multimodal learning, and utilize diffusion models to generate high-fidelity images.
Skills you'll gain
Category: Linear Algebra
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Build toward a degree
This Specialization is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
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Frequently asked questions
It is recommended that courses are taken in order.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
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