- Little to no programming knowledge is needed! I’ll be showing you everything you need to know, but basic Python is a plus
- You will need a computer running Microsoft Windows, or Linux, or a Mac running OS X.
- All the software needed in this course is free and open source! I provide all images, models and classifiers used in this course
- A webcam to implement some of the mini projects
Learn Computer Vision using OpenCV in Python – using the latest 2018 concepts and implement 12 awesome projects!
What previous students have said:
“I’m amazed at the possibilities. Very educational, learning more than what I ever thought was possible. Now, being able to actually use it in a practical purpose is intriguing… much more to learn & apply”
“Extremely well taught and informative Computer Vision course! I’ve trawled the web looking for Opencv python tutorials resources but this course was by far the best amalgamation of relevant lessons and projects. Loved some of the projects and had lots of fun tinkering them.”
“Awesome instructor and course. The explanations are really easy to understand and the materials are very easy to follow. Definitely a really good introduction to image processing.”
“I am extremely impressed by this course!! I think this is by far the best Computer Vision course on Udemy. I’m a college student who had previously taken a Computer Vision course in undergrad. This 6.5 hour course blows away my college class by miles!!”
“Rajeev did a great job on this course. I had no idea how computer vision worked and now have a good foundation of concepts and knowledge of practical applications. Rajeev is clear and concise which helps make a complicated subject easy to comprehend for anyone wanting to start building applications.”
Why Learn Computer Vision in Python using OpenCV?
Computer vision applications and technology are exploding right now! With several apps and industries making amazing use of the technology, from billion dollar apps such as Pokémon GO, Snapchat and up and coming apps like MSQRD and PRISMA.
Even Facebook, Google, Microsoft, Apple, Amazon, and Tesla are all heavily utilizing computer vision for face & object recognition, image searching and especially in Self-Driving Cars!
As a result, the demand for computer vision expertise is growing exponentially!
However, learning computer vision is hard! Existing online tutorials, textbooks, and free MOOCs are often outdated, using older an incompatible libraries or are too theoretical, making it difficult to understand.
This was my problem when learning Computer Vision and it became incredibly frustrating. Even simply running example code I found online proved difficult as libraries and functions were often outdated.
I created this course to teach you all the key concepts without the heavy mathematical theory while using the most up to date methods.
I take a very practical approach, using more than 50 Code Examples.
At the end of the course, you will be able to build 12 Awesome Computer Vision Apps using OpenCV in Python.
I use OpenCV which is the most well supported open source computer vision library that exists today! Using it in Python is just fantastic as Python allows us to focus on the problem at hand without being bogged down by complex code.
If you’re an academic or college student I still point you in the right direction if you wish to learn more by linking the research papers of techniques we use.
So if you want to get an excellent foundation in Computer Vision, look no further.
This is the course for you!
In this course, you will discover the power of OpenCV in Python, and obtain skills to dramatically increase your career prospects as a Computer Vision developer.
You will learn:
- The key concepts of Computer Vision & OpenCV.
- To perform image manipulations such as transformations, cropping, blurring, thresholding, edge detection and cropping.
- To segment images by understanding contours, circle, and line detection. You’ll even learn how to approximate contours, do contour filtering and ordering as well as approximations.
- Use feature detection (SIFT, SURF, FAST, BRIEF & ORB) to do object detection.
- Implement Object Detection for faces, people & cars.
- Extract facial landmarks for face analysis, applying filters and face swaps.
- Implement Machine Learning in Computer Vision for handwritten digit recognition.
- Implement Facial Recognition.
- Implement and understand Motion Analysis & Object Tracking.
- Use basic computational photography techniques for Photo Restoration (eliminate marks, lines, creases, and smudges from old damaged photos).
- How to become a true computer vision expert by getting started in Deep Learning
- 12 Cool computer vision startup ideas
As for Updates and support:
I will be continuously adding updates, fixes, and new amazing projects every month!
I will be active daily in the ‘questions and answers’ area of the course, so you are never on your own.
So, are you ready to get started? Enroll now and start the process of becoming a master in Computer Vision today!
Who this course is for:
- Beginners who have an interest in computer vision
- College students looking to get a head start before starting computer vision research
- Anyone curious using Deep Learning for Computer Vision
- Entrepreneurs looking to implement computer vision startup ideas
- Hobbyists wanting to make a cool computer vision prototype
- Software Developers and Engineers wanting to develop a computer vision skillset
Created by Rajeev Ratan
Last updated 11/2018
Size: 1.05 GB