92% off #Introduction to Computer Vision | Master OpenCV in Python – $10

Build 12 Python apps: such as Augmented Reality Face Filters (Snapchat/MSQRD), Face Recognition & car/people detection!

All Levels,  –   6.5 hours,  65 lectures 

Average rating 4.5/5 (4.5 (102 ratings) Instead of using a simple lifetime average, Udemy calculates a course’s star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.)

Course requirements:

Basic Python is a plus, but no programming knowledge is needed! I’ll be showing you everything you need to know.
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 opensource! I provide all images, models and classifiers used in this course!
A webcam to implement some of the mini projects

Course description:

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What previous students have said:

“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.”

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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 heavily mathematical theory while using the most up to date methods. 

I take a very practical approach, using almost 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.

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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

Reviews:

“This video was unclear to me. It takes me time to apply it. More specifically:
1. The video doesn’t explain how to install both versions of anaconda and python.
Not clear which one to use first.
2. No explanation of what is Jupyter and why do we need it.
3. No explanation of how to direct Jupyter to use the example files (the downloaded file) in a custom directory that is not within the users folder.” (Ey)

“Great course : computer vision is a very interesting topic with a lot of applications. The course covers the basics, from images manipulations, face detection/recognition, motion tracking, machine learning. Python + numpy knowledge is required. Each section ends with a very fun ‘project/app’ and there are many references/links to research/papers for people (I am) about the theory.
Thanks Rajeev for the awesome course, I learned a lot.” (Jonathan Bouchet)

“Easy language and very nice introduction to OpenCV” (Fabio Miyauchi)

 

 

About Instructor:

Rajeev Ratan

Hi I’m Rajeev and I love Data Science and Computer Vision.
I have a BSc in Computer & Electrical Engineering and MSc in Artificial Intelligence from the University of Edinburgh where I gained extensive knowledge machine learning, computer vision and intelligent robotics.
I have published research on using data-driven methods for probabilistic stochastic modeling and even was part of a group that won a robotic competition at the University of Edinburgh.
I’ve attempted my own computer vision startup that was based around deep learning. I’ve been contributing to 2 more startups in computer vision domains.
Previously, I worked at two of the Caribbean’s largest telecommunication operators where he gained experience in managing technical staff and deploying complex telecommunications projects.

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Introduction to Computer Vision | Master OpenCV in Python
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