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Computer vision courses

248 Courses

California Institute of Technology Coursera

Computer Vision: YOLO Custom Object Detection with Colab GPU

Computer Vision: YOLO Custom Object Detection with Colab GPU In this comprehensive course, you'll dive into the world of real-time object detection with YOLO, one of the most powerful algorithms for detecting objects in images and videos. The course begins with an introduction to YOLO and object detection, followed by setting up your development.
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California Institute of Technology Coursera

Build a DIY Multimodal Question Answering System with Vertex AI

Build a DIY Multimodal Question Answering System with Vertex AI Join this self-paced lab in the Google Cloud console to master building a question answering system capable of understanding both text and images with Vertex AI. Perfect for those interested in Artificial Intelligence, Machine Learning, Computer Vision, and Google Cloud Platform..
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California Institute of Technology Coursera

Introduction to Vertex AI Embeddings: Text and Multimodal

Introduction to Vertex AI Embeddings: Text and Multimodal Explore the comprehensive world of Vertex AI with our self-paced lab, Introduction to Vertex AI Embeddings: Text and Multimodal. Conducted through the Google Cloud console, this lab provides hands-on experience with the Vertex AI Embeddings API, catering to both Text and Multimodal (Image.
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California Institute of Technology Coursera

Multimodal Generative AI: Vision, Speech, and Assistants

Multimodal Generative AI: Vision, Speech, and Assistants We are introducing a new course to replace the "Coding with ChatGPT" course in the Generative AI specialization. This updated course will cover materials, models, and content released in 2024. Some of the new additions include material on using AI for image-to-text (vision), text-to-speech,.
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California Institute of Technology Coursera

Computer Vision: Face Recognition Quick Starter in Python

Computer Vision: Face Recognition Quick Starter in Python This comprehensive course guides you through the fascinating world of face recognition using Python. Starting with an introduction to face recognition concepts, you'll proceed to set up your environment using Anaconda and address any initial setup challenges. The course then delves into.
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California Institute of Technology University of California, Davis

Hands-on Data Centric Visual AI

Hands-on Data Centric Visual AI This comprehensive course is a hands-on guide to developing and maintaining high-quality datasets for visual AI applications. Learners will gain in-depth knowledge and practical skills in: Discovering and implementing various labeling approaches, from manual to fully automated methods Assessing and impro.
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California Institute of Technology AWS Skill Builder

Computer Vision with GluonCV

Computer Vision with GluonCV In this course, you will build a useful understanding of the components of a convolutional neural network (CNN) like convolutions and pooling layers, etc. In this course, Alex Smola and Tong He show how to implement some computer vision techniques using GluonCV, a computer vision toolkit. Intended Audience This cour.
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provider AWS Skill Builder
pricing Free Certificate
duration 2 hours
sessions On-Demand
California Institute of Technology Udemy

Learn Computer Vision with OpenCV Library using Python

Unlock the potential of computer vision with our comprehensive course on OpenCV using Python. Hosted on Udemy, this unique training opportunity is perfect for those interested in delving into the realms of Python programming, computer vision, and image processing. By enrolling in our course, you'll gain hands-on experience in constructing a sophist.
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provider Udemy
pricing Free Online Course
duration 1-2 hours
sessions On-Demand
California Institute of Technology Stanford University

Introduction to Artificial Intelligence

Embark on a journey into the fascinating world of Artificial Intelligence (AI) with our introductory course. This foundational program delves into the basics of AI and explores its various applications, aiming to spark your interest in the endless possibilities that AI has to offer. AI's scope extends far beyond our current capabilities, constantly.
provider Udacity
pricing Free Online Course
duration 16 weeks, 6 hours a week
sessions On-Demand
California Institute of Technology Coursera

Recognizing Shapes in Images with OpenCV

Title: Recognizing Shapes in Images with OpenCV Description: Dive into the world of computer vision with this comprehensive 1.5-hour project-based course, where you'll harness the power of image processing techniques to identify shapes using Hough transforms. Perfect for enthusiasts eager to delve into real-world applications, this course equips yo.
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provider Coursera
pricing Paid Course
duration 1-2 hours
sessions On-Demand

“Computer vision student” sounds like a quote from science fiction, don’t you think? In fact, a computer vision engineer is a profession that, although it has not yet become the most widespread, is already rapidly gaining popularity and offers high salaries even at the start of a career.

What is computer vision and what does its developer do?

A computer vision engineer is a specialist who teaches computers to extract information from images. In particular, automatically recognize objects or gestures in images and videos. If a person can visually determine something (for example, find a defect in a product), a computer can also be trained to do this - and thus save time and resources, simplifying many processes.

Developments in the field of computer vision courses are used in a wide variety of companies whose products are related to images or video. This includes the production of self-driving cars, helping doctors interpret MRI images when searching for tumors, and even facial recognition in the subway to identify violators of the self-isolation regime. Computer vision specialists help many e-commerce businesses reduce the burden of moderation: for example, when an ad service like Avito fights trolls who upload pictures with inappropriate content.

Computer vision specialists after computer vision courses are called differently: developers, engineers, and researchers (computer vision scientist). Essentially, a computer vision specialist is more of an engineer who uses mathematics and programming as working tools. So, globally, a computer vision engineer, a computer vision scientist, a computer vision developer and a technical vision developer are one and the same thing.

What does a computer vision developer actually do?

As a rule, the day of such a specialist begins with a stand-up with the team. He then writes code to train neural networks, preprocesses data, and analyzes experiments. A computer vision developer can work alone or in a team, where everyone performs part of a larger task.

As for working tools, the Python language is usually used to write code for experiments, and the Tensorflow or Pytorch frameworks are used to train neural networks. The work also involves special libraries for image processing such as OpenCV. For high-load projects, the C++ language can also be used, since anything written in it is executed many times faster.

Computer vision is a young, dynamically developing field at the intersection of science and engineering, in which there are still more experiments than ready-made solutions. To grow, a specialist here needs to constantly learn. But it is the novelty and non-standard nature of the tasks, as well as the opportunity to create something truly innovative, that brings many people into this profession.

What do they teach in computer vision classes at AI Education?

Training at the best computer vision course typically consists of three modules: creating infrastructure, basics of machine learning and studying computer vision.

The first block at a computer vision online course can be called introductory. Since specialists in the field of computer vision rely on knowledge of mathematics and programming when solving problems, at the start they will have to study from scratch or brush up on topics from higher mathematics, mathematical analysis and linear algebra, as well as work with the Python language. Don’t worry if your knowledge is limited to school mathematics, which was “long ago and not true”: we will help you improve the necessary topics in the first module, so that in the future all students can move through the program at the same rhythm.

The second module is entirely devoted to machine learning. It helps solve computer vision problems faster and easier. For example, for facial recognition, you can expertly describe facial features based on questions that are asked when compiling an identikit. Or you can feed the algorithm a lot of photographic portraits with markings about which face belongs to whom, and then the algorithm itself will learn to extract features by which faces can be identified. In the future, if you need to determine who is in the photo, the algorithm will only need a database of portraits. If there is a photo of the person you need, the system itself will easily find him.

In the second module you will examine probability theory and mathematical statistics. Students will practice solving problems using fundamental algorithms and data structures in Python, become familiar with Python libraries for Data Science (NumPy, Matplotlib), as well as machine learning algorithms.

Finally, in the third module at this machine vision course you will analyze the main tasks of computer vision, we will work with mathematical morphology and the OpenCV and PIL libraries!