Introduction to Computer Vision and Image Processing
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.As part of this course, you will utilize Python, Pillow, and OpenCV for basic image processing and perform image classification and object detection.
This is a hands-on course and involves several labs and exercises. Labs will combine Jupyter Labs and Computer Vision Learning Studio (CV Studio), a free learning tool for computer vision. CV Studio allows you to upload, train, and test your own custom image classifier and detection models. At the end of the course, you will create your own computer vision web app and deploy it to the Cloud.
This course does not require any prior Machine Learning or Computer Vision experience. However, some knowledge of the Python programming language and high school math is necessary.
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Syllabus
Syllabus - What you will learn from this course
Week 1
Introduction to Computer Vision
Week 2
Image Processing with OpenCV and Pillow
Week 3
Machine Learning Image Classification
Week 4
Neural Networks and Deep Learning for Image Classification
Week 5
Object Detection
Week 6
Project Case: Not Quite a Self-Driving Car - Traffic Sign Classification
FAQ
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
What will I get if I subscribe to this Certificate?
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What will I be able to do after completing this course?
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
Are there any software or hardware pre-requistes for this course?
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).
Are there any pre-requisties or prior experience necssary for this course?
Some programming knowledge, especially with Python is needed to complete this course. The following course equips you with the necessary Python background:
- Python for Data Science
Reviews
Great introduction to Visual Recognition and Computer Vision! Lots of examples are provided for me to grasp the concepts behind complicated applications!
Course is good but Watson service in IBM Cloud ran into issues repeatedly, Unfortunately! I hope IBM and community will be able to support and guide better. Thanks for the course.
There was a few bugs, but some I was able to get around, others weren't important. The course was very interesting and a good foundation into computer vision
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.
