Current

Intermediate

40% Off

Deep Learning for Computer Vision

Coursera · University of Colorado Boulder · Updated March 2026

Platform rating

4.5/5

AI Tutor Rating

8.3/10

Duration

1-4 weeks

Classes

36

Learn deep learning techniques for computer vision including autoencoders, CNNs, and GANs with hands-on implementation.

What you'll get

Build CNNs for image classification

Implement autoencoders and GANs

Deploy computer vision models

Course Curriculum

8 chapters

Fit

Best for

ML Engineers

Data Scientists

AI Researchers

Deep Learning Practitioners

Not ideal for

Learners seeking only entry-level overviews

Prerequisites & pricing

Prerequisites

Python, basic ML knowledge

Pricing

Subscription

Certification

Certificate

Growth Leverage: Completing this course positions individuals for roles such as Computer Vision Engineer, Machine Learning Engineer, or Data Scientist, equipping them with the knowledge to pursue advanced certifications like TensorFlow Developer or Visual Recognition Specialist, thus expanding their career opportunities in the AI and ML industry.

Skills Value: The practical skills gained, such as building CNNs and deploying GANs, are in high demand, with employers offering salary premiums of up to 20% above average for professionals adept in computer vision, addressing critical challenges in automation and data analysis across various sectors.

CNN

Computer Vision

Deep Learning

GANs

Autoencoders

ShareCompare

Add to StackGo to Course

Alternatives to Deep Learning for Computer Vision

Compare

Current

Real-Time Video AI on NVIDIA GPUs

NVIDIA Deep Learning Institute (DLI) · NVIDIA

4.6

8.6/10

8 hours self-paced

Build real-time video AI pipelines using NVIDIA DeepStream and TensorRT. Process multiple video streams with GPU-accelerated inference.

$90

ViewAddCompare

In this hands-on class project, you will learn to use Python and OpenCV to extract individual frames from a video file. This project will introduce you to basic ...

In this class, you will create a simple document scanner using the OpenCV library and Python. This can be useful, for example, for scanning pages in a book.

In this hands-on project-based course, you'll learn how to build a real-time multi-face recognizer using Python, OpenCV, and the face_recognition library.