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IBM

Introduction to Computer Vision and Image Processing

IBM via Coursera

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Overview

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Unlock the power of computer vision to add intelligence to images and videos! This course equips you with practical skills to understand and apply computer vision (CV)—a rapidly growing branch of AI and machine learning that drives innovations from self-driving cars to augmented reality. Through guided, hands-on labs using Python, Pillow, and OpenCV, you’ll perform essential image processing tasks such as filtering, enhancement, classification, and object detection—all within JupyterLab Notebooks for a seamless learning experience. By the end of the course, you’ll apply transfer learning with a pre-trained deep neural network to build an image classification model, experimenting with different hyperparameters to enhance its performance on a provided dataset. To take this, you need to have a foundational knowledge of Python, machine learning, and deep learning. In just a few weeks, you’ll learn to turn pixels into insights and launch your journey into AI-powered visual intelligence. Enroll today and start creating the future with computer vision!

Syllabus

  • Introduction to Computer Vision
    • 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.
  • Image Processing with OpenCV and Pillow
    • 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.
  • Machine Learning Image Classification
    • 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.
  • Neural Networks and Deep Learning for Image Classification
    • 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.
  • Object Detection
    • 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.
  • Project Case: Not Quite a Self-Driving Car - Traffic Sign Classification
    • 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.

Taught by

Yi Leng Yao and Sacchit Chadha

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Reviews

5.0 rating, based on 2 Class Central reviews

4.3 rating at Coursera based on 1431 ratings

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  • Profile image for APPALA BHARATH
    APPALA BHARATH
    "This is a very good course with clear explanations and practical examples. The content is well structured and easy to understand. I learned a lot and would definitely recommend it."
  • Aman Parkash
    Good to be updated on the changes that have taken place. Top marks for the instructor.” “Overall the course is going to be very useful in my day to day job.” “Really well done; made a dry subject really interesting, and clearly has a passion for traffic management that shone through!
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