Computer vision courses

202 Courses

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.

Pluralsight

Image Generation with DALL-E 3

Image Generation with DALL-E 3 Image-based generative AI allows the creation of stunning and useful images via prompts. This course will teach you how to generate high-quality images using the power of Generative AI with DALL-E. In this course, Image Generation with DALL-E, you’ll learn how to harness the power of generative AI for image creati.

Coursera

Intro to Artificial Intelligence on Microsoft Azure

Intro to Artificial Intelligence on Microsoft Azure Whether you're just beginning to work with Artificial Intelligence (AI) or you already have AI experience and are new to Microsoft Azure, this course provides you with everything you need to get started. Artificial Intelligence (AI) empowers amazing new solutions and experiences; and Microsoft Az.

Northeastern University

Practical AI for Professionals

Practical AI for Professionals Explore key ideas in Artificial Intelligence (AI) while delving into trending developments in the field. This course examines AI tools and frameworks to enable effective and efficient collaboration across technical and non-technical stakeholders. Analyze topics such as AI-enabled perception, representation, reason.

Coursera

Building AI Cloud Apps with Microsoft Azure

Building AI Cloud Apps with Microsoft Azure Discover the comprehensive nine-course program, Building AI Cloud Apps with Microsoft Azure, designed to equip you with the skills to develop cutting-edge cloud and AI solutions using Microsoft Azure. Offered by Coursera, this program covers a wide range of topics including Azure Functions, A.

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.

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.

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,.

Pluralsight

Microsoft Certified: Azure Azure AI Engineer Associate (AI-102): Exam Preparation

The AI-102 certification exam tests your ability to build, manage, and deploy AI solutions using Azure AI. This course equips you with the knowledge needed to tackle the exam confidently. Are you ready to take on the Microsoft Azure AI Engineer Associate Exam? In the course titled "Microsoft Certified: Azure AI Engineer Associate (AI-102): Exam.

AWS Skill Builder

AWS Cloud Quest: Machine Learning (Japanese)

Explore the dynamic world of cloud computing and machine learning with AWS Cloud Quest: Machine Learning - offered in Japanese by AWS Skill Builder. This course is your gateway to mastering essential concepts such as: Cloud computing fundamentals with Amazon S3. Introductory cloud steps using Amazon EC2 and AWS infrastructure. Estimat.

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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!