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
Access provided by Amrita Vishwa Vidyapeetham
102,295 already enrolled
Gain insight into a topic and learn the fundamentals.
4.3
(1,399 reviews)
Beginner level
No prior experience required
Flexible schedule
2 weeks at 10 hours a week
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.
Skills you'll gain
- Machine Learning Algorithms
- Image Analysis
- Algorithms
- Deep Learning
- Supervised Learning
- Jupyter
- Machine Learning
- Artificial Neural Networks
- Data Processing
- Applied Machine Learning
- Cloud Applications
- Application Deployment
- Visualization (Computer Graphics)
- Computer Programming
- Cloud Development
- Computer Vision
- Artificial Intelligence and Machine Learning (AI/ML)
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
5 videos3 readings2 assignments
Image processing enhances images or extracts useful information from them. In this module, we will learn the basics of image processing with Python libraries OpenCV and Pillow.
What's included
6 videos1 reading2 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 videos1 reading2 assignments5 app items
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 videos1 reading2 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 videos2 readings2 assignments2 app items
In the final week of this course, you will build and evaluate an image classifier using transfer learning. For this project, you will train your custom model on labeled images and test its performance.
What's included
1 video2 readings1 peer review2 app items3 plugins
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4.3
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Showing 3 of 1399
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
MO
5
Reviewed on Nov 12, 2019
Thoroughly enjoyed this course. Learned about OpenCV a bit and added to my small knowledge of Python. The ability to know how to train Watson to do optical recognizition will be invaluable.
EC
5
Reviewed on Mar 15, 2022
Really structured and engaging. I'll recommend this course for beginners in computer vision. You should be interested in learning Python aswell
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