IBM
Introduction to Computer Vision and Image Processing
IBM

Introduction to Computer Vision and Image Processing

This course is part of multiple programs.

Taught in English

Some content may not be translated

Aije Egwaikhide
Joseph Santarcangelo

Instructors: Aije Egwaikhide

71,218 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.3

(1,104 reviews)

|

91%

Beginner level
No prior experience required
21 hours (approximately)
Flexible schedule
Learn at your own pace

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.

Details to know

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Assessments

10 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.3

(1,104 reviews)

|

91%

Beginner level
No prior experience required
21 hours (approximately)
Flexible schedule
Learn at your own pace

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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 quizzes1 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 quizzes9 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 quizzes6 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 quizzes6 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 quizzes3 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

Instructor ratings
4.3 (225 ratings)
Aije Egwaikhide
IBM
6 Courses575,414 learners
Joseph Santarcangelo
IBM
25 Courses1,345,674 learners

Offered by

IBM

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