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  • Computer Vision

Build Computer Vision Skills with Projects

Computer vision projects can help you learn image processing, object detection, and facial recognition techniques. You can build skills in data annotation, algorithm optimization, and integrating computer vision with machine learning models. Many courses introduce tools like OpenCV, TensorFlow, and Python libraries, showcasing how these technologies are used to create applications that analyze and interpret visual data.


Full List of Computer Vision Projects


  • Status: Free
    Free
    M

    Microsoft

    Build a computer vision app with Azure Cognitive Services

    Skills you'll gain: Application Programming Interface (API), Microsoft Azure, Cloud API, Computer Vision, Artificial Intelligence and Machine Learning (AI/ML), User Accounts, Image Analysis, Artificial Intelligence, Cloud Computing, Software Development

    4.5
    Rating, 4.5 out of 5 stars
    ·
    518 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • C

    Coursera

    Deep Learning with PyTorch : Image Segmentation

    Skills you'll gain: PyTorch (Machine Learning Library), Model Evaluation, Convolutional Neural Networks, Transfer Learning, Image Analysis, Deep Learning, Python Programming

    4.4
    Rating, 4.4 out of 5 stars
    ·
    228 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • C

    Coursera

    Object Localization with TensorFlow

    Skills you'll gain: Tensorflow, Keras (Neural Network Library), Data Synthesis, Convolutional Neural Networks, Image Analysis, Computer Vision, Artificial Neural Networks, Model Evaluation, Applied Machine Learning, Deep Learning, Machine Learning, Python Programming

    4.3
    Rating, 4.3 out of 5 stars
    ·
    117 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • C

    Coursera

    Basic Image Classification with TensorFlow

    Skills you'll gain: Model Evaluation, Keras (Neural Network Library), Tensorflow, Image Analysis, Artificial Neural Networks, Deep Learning, Computer Vision, Machine Learning, Classification Algorithms

    4.6
    Rating, 4.6 out of 5 stars
    ·
    847 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • C

    Coursera

    Deep Learning with PyTorch : Generative Adversarial Network

    Skills you'll gain: Generative Adversarial Networks (GANs), PyTorch (Machine Learning Library), Deep Learning, Convolutional Neural Networks, Image Analysis, Python Programming

    4.5
    Rating, 4.5 out of 5 stars
    ·
    99 reviews

    Intermediate · Guided Project · Less Than 2 Hours

  • C

    Coursera

    Explainable AI: Scene Classification and GradCam Visualization

    Skills you'll gain: Model Evaluation, Keras (Neural Network Library), Deep Learning, Convolutional Neural Networks, Image Analysis, Tensorflow, Computer Vision, Transfer Learning, Applied Machine Learning, Machine Learning, Data Preprocessing, Python Programming

    4.6
    Rating, 4.6 out of 5 stars
    ·
    59 reviews

    Intermediate · Guided Project · Less Than 2 Hours

What brings you to Coursera today?

  • C

    Coursera

    Fake News Detection with Machine Learning

    Skills you'll gain: Recurrent Neural Networks (RNNs), Exploratory Data Analysis, Deep Learning, Text Mining, Matplotlib, Data Cleansing, Data Analysis, Data Preprocessing, Natural Language Processing, Data Manipulation, Python Programming, Machine Learning, Model Evaluation

    4.6
    Rating, 4.6 out of 5 stars
    ·
    264 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • C

    Coursera

    SketchUp: how to start modelling simple 3D objects

    Skills you'll gain: 3D Modeling, SketchUp (3D Modeling Software), Computer Graphics, Visualization (Computer Graphics), 3D Assets, Design Software, Graphical Tools, Product Design, Graphic Design

    4.5
    Rating, 4.5 out of 5 stars
    ·
    362 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • C

    Coursera

    Performance Testing with JMeter for absolute beginners

    Skills you'll gain: Performance Testing, Test Tools, API Testing, Test Execution Engine, Test Data

    4.6
    Rating, 4.6 out of 5 stars
    ·
    81 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • C

    Coursera

    Business Analysis & Process Management

    Skills you'll gain: Process Analysis, Business Process, Business Analysis, Workflow Management, Process Management, Process Flow Diagrams, Business Process Modeling, Stakeholder Management, Stakeholder Analysis, Computer Literacy

    4.5
    Rating, 4.5 out of 5 stars
    ·
    7.4K reviews

    Beginner · Guided Project · Less Than 2 Hours

  • C

    Coursera

    Terraform for absolute beginners

    Skills you'll gain: Terraform, Infrastructure as Code (IaC), Infrastructure As A Service (IaaS), Microsoft Azure, IT Automation, DevOps

    4.3
    Rating, 4.3 out of 5 stars
    ·
    194 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • C

    Coursera

    API Testing a real web application via Postman

    Skills you'll gain: API Testing, Postman API Platform, OAuth, Application Programming Interface (API), Performance Testing, Security Testing, Software Testing, Restful API, Scenario Testing

    4.4
    Rating, 4.4 out of 5 stars
    ·
    71 reviews

    Beginner · Guided Project · Less Than 2 Hours

Searches related to computer vision

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1234…49

In summary, here are 10 of our most popular computer vision courses

  • Build a computer vision app with Azure Cognitive Services: Microsoft
  • Deep Learning with PyTorch : Image Segmentation : Coursera
  • Object Localization with TensorFlow: Coursera
  • Basic Image Classification with TensorFlow: Coursera
  • Deep Learning with PyTorch : Generative Adversarial Network: Coursera
  • Explainable AI: Scene Classification and GradCam Visualization : Coursera
  • Fake News Detection with Machine Learning: Coursera
  • SketchUp: how to start modelling simple 3D objects: Coursera
  • Performance Testing with JMeter for absolute beginners: Coursera
  • Business Analysis & Process Management: Coursera

Skills you can learn in Software Development

Programming Language (34)
Google (25)
Computer Program (21)
Software Testing (21)
Web (19)
Google Cloud Platform (18)
Application Programming Interfaces (17)
Data Structure (16)
Problem Solving (14)
Object-oriented Programming (13)
Kubernetes (10)
List & Label (10)

Frequently Asked Questions about Computer Vision

Computer vision is a field of artificial intelligence that enables computers to interpret and understand visual information from the world. It involves the development of algorithms and models that allow machines to process images and videos, recognize objects, and make decisions based on visual data. The importance of computer vision lies in its wide-ranging applications across various industries, including healthcare, automotive, security, and entertainment. By automating visual tasks, computer vision enhances efficiency, accuracy, and the ability to analyze large datasets, ultimately driving innovation and improving decision-making.‎

A career in computer vision can lead to various job opportunities, including roles such as computer vision engineer, machine learning engineer, data scientist, and research scientist. These positions are in high demand as organizations increasingly rely on visual data analysis for applications like autonomous vehicles, facial recognition systems, and augmented reality. Additionally, professionals in this field may work in sectors like robotics, healthcare imaging, and surveillance, where the ability to interpret visual information is crucial.‎

To pursue a career in computer vision, you should focus on developing a strong foundation in several key skills. These include programming languages such as Python and C++, proficiency in machine learning and deep learning frameworks, and a solid understanding of image processing techniques. Familiarity with libraries like OpenCV and TensorFlow is also beneficial. Additionally, knowledge of mathematics, particularly linear algebra and calculus, is essential for understanding the algorithms that underpin computer vision technologies.‎

There are numerous online courses available for those interested in computer vision. Some of the best options include the Computer Vision Specialization, which covers fundamental concepts and advanced techniques, and the Deep Learning for Computer Vision Specialization, which focuses on applying deep learning methods to visual data. Additionally, the MathWorks Computer Vision Engineer Professional Certificate offers a comprehensive curriculum designed to equip learners with practical skills in this field.‎

Yes. You can start learning computer vision on Coursera for free in two ways:

  1. Preview the first module of many computer vision courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in computer vision, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn computer vision effectively, start by building a solid foundation in programming and mathematics. Enroll in introductory courses that cover the basics of computer vision, such as the Computer Vision Basics course. As you progress, explore more advanced topics and practical applications through specialized courses. Engage in hands-on projects to apply your knowledge, and consider collaborating with peers or joining online communities to enhance your learning experience.‎

Typical topics covered in computer vision courses include image processing techniques, feature extraction, object detection, image segmentation, and the use of convolutional neural networks (CNNs). Courses may also explore advanced topics such as 3D vision, motion analysis, and the integration of computer vision with other AI technologies. By studying these areas, you will gain a comprehensive understanding of how to analyze and interpret visual data.‎

For training and upskilling employees in computer vision, courses like the Deep Learning for Computer Vision Specialization and the First Principles of Computer Vision Specialization are excellent choices. These programs provide structured learning paths that cover both foundational concepts and advanced techniques, making them suitable for professionals looking to enhance their skills and apply computer vision in their work.‎

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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