There has been a tremendous boom in the applications of Computer Vision now a days.
The applications of Computer Vision range from understanding the environment in a Self - Driving Car to build Facial Recognition based Attention Systems for classrooms in Education Industry.
A question you might ask is: why would I even want to know about Computer Vision ? As a matter of fact, there is an undeniable demand for people who have knowledge in this domain, so that they can bring about disruptive solutions in any industry possible.
Computer Vision systems deal with high variety and volume of data, specifically images or videos.It is represented as bits and blobs which is hard to explain to a machine.As a result, these systems need intricate techniques to make sense of the data and then make data driven decisions.
This course is designed to give you a taste of how the underlying techniques work in current State - of -the - Art Computer Vision systems, and walks you through a few of the remarkable Computer Vision applications in a hands - on manner so that you can create such solutions on your own.
This is a beginner friendly course, so it does not assume any familiarity with Computer Vision or Deep Learning algorithms. But, this course assumes that you are comfortable with Python programming.
- DataHack Summit 2019 - India’s largest Applied Artificial Intelligence and Machine Learning Conference
- Getting Started
- Knowing each other
- Welcome to Computer Vision
- Documentary on Computer Vision FREE PREVIEW
- Applications of Computer Vision
- Why Computer Vision is more in Demand?
- Understand your course content
- Exercise- 4
- Getting ready for the course
- System Requirements
- Setting up the System on Cloud
- Setting up locally
- Accessing the course material
- Getting yourself ready
- Understanding the problem
- Exercise : Understanding the problem
- Introduction to Pre-trained Model FREE PREVIEW
- Exercise : Introduction to Pre-trained Model
- How to handle Image data
- Exercise : How to handle Image data
- Exploring the Emergency Classification Dataset