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Computer vision courses

186 Courses

California Institute of Technology Udemy

Building a Face Detection and Recognition Model From Scratch

Discover how to create your own Face Detection and Recognition Model with our comprehensive course. This online class, offered through Udemy, blends the intricate concepts of Machine Learning and Computer Vision, providing you with the skills needed to excel in this cutting-edge field. Whether you're interested in Python, Machine Learning, Computer.
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provider Udemy
pricing Paid Course
duration 1 hour 12 minutes
sessions On-Demand
California Institute of Technology Udemy

Deep Learning : Computer Vision Beginner to Advanced Pytorch

Embark on a transformative journey from novice to expert in the realm of computer vision with our comprehensive course, available on Udemy. Deepen your understanding of Convolutional Neural Networks (CNNs) and harness the power of Deep Learning using PyTorch and Python. This meticulously designed curriculum is tailored to infuse you with advanced s.
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provider Udemy
pricing Paid Course
duration 8 hours
sessions On-Demand
California Institute of Technology Google Cloud Skills Boost

Introduction to Computer Vision with TensorFlow

Join our cutting-edge lab, "Introduction to Computer Vision with TensorFlow," presented by Google Cloud Skills Boost. This interactive session is designed for participants to build their own computer vision model, capable of identifying various clothing items. Dive deep into the intricacies of model training and discover the factors that influence.
provider Google Cloud Skills Boost
pricing Paid Course
duration 1 hour
sessions On-Demand
California Institute of Technology AWS Skill Builder

AWS Panorama - Building Edge Computer Vision (CV) Applications

AWS Panorama: Empowering Edge CV Application Development Leverage the power of AWS Panorama to integrate computer vision into your existing on-premises camera network. Discover how to utilize AWS Panorama by installing an appliance or compatible device within your data center, seamlessly registering it with AWS Panorama, and effectively deploying c.
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provider AWS Skill Builder
pricing Free Certificate
duration 1-2 hours
sessions On-Demand
California Institute of Technology Coursera

Landing.AI for Beginners: Build Data Visualization AI Models

Embark on a journey into the realms of Computer Vision and Generative AI with the "Landing.AI for Beginners: Build Data Visualization AI Models" course. This 1-hour project-based course, available on Coursera, is your gateway to mastering the innovative LandingLens platform. You'll begin with the basics of visual prompting and swiftly move to proje.
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provider Coursera
pricing Paid Course
duration 1-2 hours
sessions On-Demand
California Institute of Technology The University of Texas at Austin

Introducción a la visión por computador: desarrollo de aplicaciones con OpenCV

Descubre el fascinante mundo de la visión por computador en nuestro nuevo curso: "Introducción a la visión por computador: Desarrollo de aplicaciones con OpenCV", ofrecido por The University of Texas at Austin en asociación con edX. Este curso está diseñado para equiparte con los conocimientos fundamentales para comprender y aplicar las técnicas de.
provider edX
pricing Free Online Course (Audit)
duration 4 weeks, 3-4 hours a week
sessions On-Demand
California Institute of Technology Universidad Autónoma de Madrid

Clasificación de imágenes: ¿cómo reconocer el contenido de una imagen?

Embark on an enlightening journey through the world of computer vision with the "Image Classification: How to Recognize the Content of an Image?" course offered by the Universidad Autónoma de Madrid on Coursera. Discover how visual content in images can be identified and classified using groundbreaking methods. This course is meticulously designed.
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provider Coursera
pricing Free Online Course (Audit)
duration 21 hours
sessions On-Demand
California Institute of Technology Institut Mines-Télécom

Traitement d'images : segmentation et caractérisation

Explorez les frontières du traitement d'images avec le MOOC "Traitement d'images : segmentation et caractérisation", offert par l'Institut Mines-Télécom sur la plateforme Coursera. Ce cours riche et transdisciplinaire puise dans les mathématiques, la physique et l'informatique pour explorer comment les images, de la production industrielle aux appl.
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provider Coursera
pricing Free Online Course (Audit)
duration 22 hours
sessions On-Demand
California Institute of Technology Institut Mines-Télécom

Traitement d'images : introduction au filtrage

Explore the World of Image Processing with Our Introduction to Filtering MOOC Dive into the interdisciplinary field of image processing that spans mathematics, physics, and computer science. From production lines to medical scanners and satellites, images are pivotal in extracting prevalent information today. They must be processed to overcome poor.
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provider Coursera
pricing Free Online Course (Audit)
duration 17 hours
sessions On-Demand
California Institute of Technology Coursera

Computer Vision in Microsoft Azure

Discover the transformative power of AI with the "Computer Vision in Microsoft Azure" course available on Coursera. This course dives into the intricacies of Microsoft Azure's Computer Vision cognitive service, which employs pre-trained models to effectively analyze images. Ideal for software developers, this course enables participants to construc.
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provider Coursera
pricing Free Online Course (Audit)
duration 8-9 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!