• views: 95
• date: 30 August 2026
• posted by: voska89

What you'll learn

Build a strong foundation in computer vision through 30 practical, hands-on projects.
Learn how to use Python, OpenCV, and other essential computer vision tools and libraries.
Understand key computer vision concepts by applying them directly to practical projects.
Gain practical experience that you can use in your portfolio and future AI/ML projects.
Work with images, videos, webcams, object detection, semantic segmentation, pose detection, image generation, image processing and feature extraction.
Learn how to build and deploy computer vision applications on edge devices.
Build real-time computer vision projects on edge using cameras and embedded devices.

Requirements

Basic knowledge of Python programming is recommended.
No prior computer vision experience is required.
Familiarity with basic programming concepts such as variables, loops, functions, and lists is helpful.
Basic knowledge of machine learning is helpful but not necessary.
(Optional) Basic electronics components and hardware, such as a Raspberry Pi, camera module, or similar edge devices, for the projects involving computer vision on edge.

Description

This course teaches you computer vision through 30 hands-on projects, taking you from foundational image processing techniques to advanced concepts. You'll learn to work with core computer vision libraries and frameworks including OpenCV, Scikit-learn, YOLO, and Detectron2, building practical skills in object detection, image classification, segmentation, and pose estimation. The course is structured to build your skills progressively, starting with fundamental image processing operations before introducing machine learning and deep learning methods for more complex visual recognition tasks.
Beyond model building, you'll gain experience integrating computer vision into applications: deploying models to the cloud with AWS, building web applications with Streamlit and Langchain, creating APIs, and running inference on edge devices. You'll also work with tools like AWS Rekognition and explore how vision models can be combined with LLMs to create more capable systems. You'll also develop the ability to evaluate and compare different approaches, understanding trade-offs between models and how much data is needed for reliable performance.
By the end of this course, you'll have the skills to design, train, and deploy computer vision systems, gaining experience across a wide range of practical use cases. Along the way, you'll strengthen your understanding of key concepts such as feature extraction, model training, and dataset preparation, giving you a well-rounded foundation in computer vision.
If you notice any tutorial makes a reference to a resource that is no longer available, or that for whatever reason the content is outdated, please let me know in the Q&A section so I can review it and fix it as soon as possible!

Who this course is for

Beginners who want to learn computer vision through practical, real-world projects.
Python developers interested in AI, computer vision, and edge AI.
Students and professionals looking to build a portfolio of computer vision projects.
AI and machine learning enthusiasts who want hands-on experience with computer vision.
Makers and developers interested in deploying computer vision applications on edge devices.
Anyone who wants to learn computer vision by building 30 practical projects from scratch.

Homepage

https://www.udemy.com/course/learn-computer-vision-in-30-days/

   
   
   

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