Computer Vision is one of the most exciting fields in Machine Learning and AI. It has applications in many industries, such as self-driving cars, robotics, augmented reality, and much more. In this beginner-friendly course, you will understand computer vision and learn about its various applications across many industries.
Introduction to Computer Vision and Image Processing
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Introduction to Computer Vision and Image Processing
Instructors: Aije Egwaikhide
86,267 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.3
(1,290 reviews)
Beginner level
No prior experience required
Flexible schedule
Approx. 21 hours
Learn at your own pace
91%
Most learners liked this course
What you'll learn
Describe the applications of computer vision across different industries.
Apply image processing and analysis techniques to computer vision problems.
Utilize Python, Pillow, and OpenCV for basic image processing and perform image classification and object detection.
Create an image classifier using Supervised learning techniques.
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Assessments
10 assignments
Taught in English
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There are 6 modules in this course
In this module, we will discuss the rapidly developing field of image processing. In addition to being the first step in Computer Vision, it has broad applications ranging anywhere from making your smartphone's image look crystal clear to helping doctors cure diseases.
What's included
4 videos2 readings2 assignments1 plugin
Image processing enhances images or extracts useful information from the image. In this module, we will learn the basics of image processing with Python libraries OpenCV and Pillow.
What's included
6 videos2 assignments9 app items
In this module, you will Learn About the different Machine learning classification Methods commonly used for Computer vision, including k nearest neighbours, Logistic regression, SoftMax Regression and Support Vector Machines. Finally, you will learn about Image features.
What's included
8 videos2 assignments6 app items2 plugins
In this module, you will learn about Neural Networks, fully connected Neural Networks, and Convolutional Neural Network (CNN). You will learn about different components such as Layers and different types of activation functions such as ReLU. You also get to know the different CNN Architecture such as ResNet and LenNet.
What's included
4 videos2 assignments6 app items1 plugin
In this module, you will learn about object detection with different methods. The first approach is using the Haar Cascade classifier, the second one is to use R-CNN and MobileNet.
What's included
2 videos1 reading2 assignments3 app items
In the final week of this course, you will build a computer vision app that you will deploy on the cloud through Code Engine. For the project, you will create a custom classifier, train it and test it on your own images.
What's included
1 peer review1 app item4 plugins
Instructors
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4.4 (267 ratings)Offered by
Recommended if you're interested in Machine Learning
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University of Michigan
University of California, Irvine
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Frequently asked questions
After completing this course you will be able to:
● explain what computer vision is and its applications
● understand the roles of Python, OpenCV and IBM Watson in computer vision
● classify images utilizing IBM Watson, Python, and OpenCV
● build and train custom image classifiers using Watson Visual Recognition API
● process images in Python using OpenCV
● create an interactive computer vision web application and deploy it to the cloud
No specialized hardware or software is required to complete this course. You will perform all labs and projects in a cloud environment and work with Python in Jupyter Notebooks, OpenCV, and IBM Watson Visual Recognition. Instructions for no-charge access to IBM Cloud is provided. You will require a modern web browser (i.e. recent versions of Chrome or Firefox).
Some programming knowledge, especially with Python is needed to complete this course. The following course equips you with the necessary Python background: