Free online courseComputer AI Vision tutorial

Duration of the online course: 3 hours and 13 minutes

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Learn the basics of computer vision and image processing with this free online course by Great Learning. Dive into convolutional neural networks and advance your AI skills.

Course Description

Welcome to the "Computer AI Vision Tutorial," an immersive and enriching course tailored for individuals who are curious about the intricacies of computer vision and its transformative potential in the realm of Artificial Intelligence. With a comprehensive duration of 3 hours and 13 minutes, this course has garnered an impressive average rating of 4 stars on a scale of 1 to 5, highlighting the high level of satisfaction and value it offers to its learners.

In this specialized course, learners will delve into the fundamental and advanced elements of computer vision, a critical subset of Artificial Intelligence under the broader category of Information Technology. As computer vision continues to revolutionize various industries, from healthcare to automotive, this tutorial provides participants with the essential knowledge and competencies required to understand and develop cutting-edge computer vision applications.

The journey begins with a deep dive into Image Processing, the cornerstone of computer vision. Learners will explore various techniques that allow computers to interpret and manipulate visual information. By understanding these foundational principles, they will grasp how images can be enhanced, analyzed, and transformed into meaningful data that drive decision-making processes in AI-driven systems.

As the course progresses, participants will be introduced to Convolutional Neural Networks (CNNs), the powerhouse behind modern computer vision advancements. CNNs mimic the human visual system and have proven to be exceptionally effective in recognizing patterns and features in images. Through practical examples and hands-on exercises, learners will acquire a thorough understanding of how CNNs operate and how they can be employed to solve real-world problems.

What sets this course apart is its structured yet flexible approach, making it accessible to beginners and beneficial to those with some prior knowledge. The "Computer AI Vision Tutorial" seamlessly integrates theoretical concepts with practical applications, ensuring that learners not only comprehend the academic aspects but also gain experience in implementing computer vision solutions.

Renowned for its commitment to quality education and industry relevance, this course stays current with the latest trends and technologies in Artificial Intelligence. Participants will emerge from this tutorial equipped with the skill sets and confidence to tackle complex computer vision challenges, whether in academic research, professional environments, or personal projects.

Enrolling in the "Computer AI Vision Tutorial" offers an opportunity to be part of the dynamic field of Artificial Intelligence, where the possibilities are endless, and the demand for skilled professionals continues to grow. Embrace this chance to enhance your knowledge and propel your career forward with insights from leading experts in AI and computer vision.

Course content

  • Video class: Computer Vision Tutorial | Image Processing | Convolution Neural Network | Great Learning 3h13m
  • Exercise: In the context of computer vision, which of the following techniques is NOT typically used to reduce the dimensions of an image before processing it with a neural network?
  • Exercise: Which of the following describes a benefit of using Convolutional Neural Network (CNN) architectures for image recognition tasks?
  • Exercise: What is the primary function of the dropout technique in a Convolutional Neural Network (CNN)?
  • Exercise: Computer vision involves the ability of computers or machines to interpret visual information from the world. One important application of computer vision is image classification. Considering the advancement in convolutional neural network (CNN) architectures, which component has significantly helped in improving the performance of deep neural networks for computer vision tasks?
  • Exercise: What is the primary purpose of applying pooling operations, such as max pooling or average pooling, in a convolutional neural network (CNN)?
  • Exercise: What is the primary advantage of employing a pre-trained model like VGG16, AlexNet, or ResNet in computer vision tasks?

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This free course includes:

3 hours and 13 minutes of online video course

Certificate of course completion

Exercises to train your knowledge

100% free, from content to certificate

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