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Computer vision courses

326 Courses

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California Institute of Technology Coursera

AI Applications: Computer Vision and Speech Recognition

Welcome to AI Applications: Computer Vision and Speech Recognition. This course offers hands-on expertise in using cutting-edge technologies for processing visual data and interpreting human speech. Gain practical skills to tackle real-world challenges in computer vision and speech analysis. By the end of this course, you will be able to:.
provider Coursera
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California Institute of Technology Coursera

Mastering AI: Neural Nets, Vision System, Speech Recognition

Embark on your AI journey with the Mastering AI specialization, tailored for both newcomers and experienced professionals. Gain essential skills in artificial intelligence, machine learning, and deep learning to create cutting-edge solutions. Delve into critical concepts such as neural networks, statistical foundations, predictive modeling, a.
provider Coursera
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California Institute of Technology Udemy

Learn Computer Vision | Python Image Automation Examples

Unlock the potential of computer vision with our comprehensive Python course on Udemy, focusing on practical image automation examples. This course is designed to provide you with the skills to master the art of image processing, utilizing powerful libraries and tools such as OpenCV. Delve into an array of topics spanning Python Courses, Comput.
provider Udemy
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California Institute of Technology Universidad Carlos iii de Madrid

Text Analytics with Python

Text Analytics with Python | Universidad Carlos III de Madrid Delve into the essential techniques of text analytics and natural language processing (NLP) with Python. This comprehensive certificate program, "Text Analytics with Python," provided by Universidad Carlos III de Madrid via edX, emphasizes both practical and scientific approaches. On.
provider edX
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California Institute of Technology Universidad Carlos iii de Madrid

Text Analytics with Python

Learn the core techniques of text analytics and natural language processing (NLP) while discovering the cognitive science that makes it possible in this certificate Text Analytics with Python. On the practical side, you’ll learn how to actually do an analysis in Python: creating pipelines for text classification and text similarity using machine l.
provider edX
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California Institute of Technology FutureLearn

Artificial Intelligence for Everyone

Discover essential AI techniques with Universiti Malaya On this eight-week course from Universiti Malaya, you’ll gain a solid understanding of artificial intelligence. Tailored specifically for beginners, you’ll be guided through essential AI concepts such as machine learning, natural language processing, and computer vision. By the end, you’ll ha.
provider FutureLearn
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California Institute of Technology FutureLearn

Artificial Intelligence for Healthcare

Delve into the transformative power of AI in the healthcare industry On this eight-week course, you’ll explore how AI is being used to turn the healthcare industry into a highly data-driven field. You’ll discover how different technologies are revolutionising areas in healthcare such as diagnostics, personalised medicine, and robotic surgery. With.
provider FutureLearn
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California Institute of Technology Udemy

AI & ML Made Easy: From Basic to Advanced (2025)

Learn AI and Machine Learning Fundamentals to Advanced Deep Learning with Real-World Projects
provider Udemy
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California Institute of Technology edX

Azure AI Fundamentals: Build & Deploy AI

This course offers a comprehensive introduction to artificial intelligence (AI) concepts and the array of Azure services available for developing AI solutions. Designed for individuals with both technical and non-technical backgrounds, this course requires no prior experience in data science or software engineering. It is ideal for professionals l.
provider edX
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California Institute of Technology Microsoft Learn

Create computer vision solutions in Azure

Module 1: Analyze imagesAfter completing this module, you'll be able to: Provision an Azure AI Vision resource Analyze an image Generate a smart-cropped thumbnail Module 2: Classify imagesAfter completing this module, you will be able to: Provision Azure resources for Azure AI Custom Vision Understand image classification Train an image classif.
provider Microsoft Learn

“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!