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
Introduction to Computer Vision and Image Processing
Instructors: Aije Egwaikhide
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Gain insight into a topic and learn the fundamentals.
4.3
(1,398 reviews)
Beginner level
No prior experience required
Flexible schedule
2 weeks at 10 hours a week
Learn at your own pace
92%
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.
Skills you'll gain
- Image Analysis
- Artificial Neural Networks
- Deep Learning
- Computer Programming
- Application Deployment
- Data Processing
- Artificial Intelligence and Machine Learning (AI/ML)
- Machine Learning
- Algorithms
- Cloud Applications
- Applied Machine Learning
- Visualization (Computer Graphics)
- Cloud Development
- Computer Vision
- Jupyter
- Supervised Learning
- Machine Learning Algorithms
Details to know
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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
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4.4 (304 ratings)Offered by
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4.3
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Showing 3 of 1398
SK
5
Reviewed on May 19, 2021
very informative course which truly helped me learn .The labs service however is very bad but teaching staff is always there to help
SB
5
Reviewed on Jul 1, 2020
Well Explained about each and every tools and how to train and test the models and deploy into the cloud services I had very good expreience in collabrating into IBM
AG
4
Reviewed on Dec 17, 2019
much better use of notebooks, and good to be able to compare openCV's (free) capabilities and use Watson computer vision directly via API. Liked the classroom videos too.
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