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Computer vision courses

289 Courses

California Institute of Technology Udemy

OpenCV. Curso de OpenCV para visión por ordenador con Python

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provider Udemy
pricing Paid Course
duration 3-4 hours
sessions On-Demand
California Institute of Technology Udemy

OpenCV ile Görüntü İşleme 2/3 (Python)

OpenCV ile Görüntü İşleme 2/3 (Python) Python Programlama Dili Kullanarak OpenCV ile Görüntü İşleme Dersi | Başlangıçtan İleri Düzeye | 2.Video Eğitimidir. University: Provider: Udemy Categories: Artificial Intelligence Courses, Python Courses, Computer Vision Courses, Data Science Courses, OpenCV Courses, Object Detection Courses
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provider Udemy
pricing Free Online Course
duration 2-3 hours
sessions On-Demand
California Institute of Technology Udemy

OpenCV ile Görüntü İşleme 1/3 (Python)

OpenCV ile Görüntü İşleme 1/3 (Python) | Udemy Python Programlama Dili kullanarak OpenCV ile Görüntü İşleme dersi. Başlangıçtan ileri düzeye 1. video eğitimidir. University: Udemy. Bu kurs ile görüntü işleme dünyasına adım atın ve OpenCV ile yeteneklerinizi geliştirin. Kategori: Yapay Zeka Kursları, Python Kursları, Bilgisayarla Görme Kursları,.
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provider Udemy
pricing Free Online Course
duration 1-2 hours
sessions On-Demand
California Institute of Technology Udemy

OpenCV + Webapp

OpenCV + Webapp | Face & Eye Detection Course by Udemy Unlock the potential of computer vision with our OpenCV + Webapp course. This comprehensive training focuses on face and eye detection via a cutting-edge web application. Provider: Udemy Categories: Python Courses, Computer Vision Courses, OpenCV Courses, Linux Courses, Web Development Course.
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provider Udemy
pricing Paid Course
duration 1-2 hours
sessions On-Demand
California Institute of Technology Udemy

Augelab Studio No-Code Görüntü İşleme ve Yapay Zeka Eğitimi

Augelab Studio No-Code Görüntü İşleme ve Yapay Zeka Eğitimi Görsel işleme ve yapay zeka uygulamaları geliştirin, hem de kodlama bilgisine ihtiyaç duymadan! Udemy tarafından sunulan bu eğitim, en hızlı ve en kolay yoldan no-code yöntemleriyle görüntü işleme ve yapay zeka projeleri oluşturmanızı sağlar. Kategori: Yapay Zeka Kursları, Bilgisayarla G.
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provider Udemy
pricing Paid Course
duration 4-5 hours
sessions On-Demand
California Institute of Technology

Computer Vision with GluonCV (Spanish)

Computer Vision with GluonCV (Spanish) Descripción En este curso, obtendrá conocimientos útiles sobre los componentes de una red neuronal convolucional (CNN), como las convoluciones y las capas de agrupación, entre otros. Alex Smola y Tong He muestran cómo implementar algunas técnicas de visión artificial con GluonCV, un conjunto de herramientas d.
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California Institute of Technology AWS Skill Builder

Introduction to AWS DeepLens (Spanish)

Introducción a AWS DeepLens (Español) Este es un curso introductorio sobre AWS DeepLens, la primera cámara de video del mundo habilitada para aprendizaje profundo. En este curso se revisa el hardware del dispositivo, su arquitectura y las plantillas de proyecto de muestra de AWS DeepLens, que le permiten comenzar a crear aplicaciones de visión a.
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provider AWS Skill Builder
pricing Free Certificate
sessions On-Demand
California Institute of Technology AWS Skill Builder

Seeing Clearly: Computer Vision Theory (Spanish)

Seeing Clearly: Computer Vision Theory (Spanish) Estos cursos analizan la manera en la que las máquinas logran comprender imágenes y videos. Abarcaremos temas de visión artificial como el reconocimiento automático de objetos y actividades y la primera cámara de aprendizaje profundo del mundo, por no mencionar las demostraciones de casos de uso co.
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provider AWS Skill Builder
pricing Free Certificate
duration 2-3 hours
sessions On-Demand
California Institute of Technology University of Michigan

Python Project: pillow, tesseract, and opencv

Python Project: pillow, tesseract, and opencv This course will walk you through a hands-on project suitable for a portfolio. You will be introduced to third-party APIs and will be shown how to manipulate images using the Python imaging library (pillow), how to apply optical character recognition to images to recognize te.
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California Institute of Technology Coursera

Fundamentals of CNNs and RNNs

Fundamentals of CNNs and RNNs This course covers fundamental concepts of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are widely used in computer vision and natural language processing areas. In the CNN part, you will learn the concepts of CNNs, the two major operators (convolution and pooling), and t.
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provider Coursera
pricing Free Online Course (Audit)
duration 5-6 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!