The Complete Computer Vision Course with Python

Learn the technologies behind the next generation of consumer and enterprise applications. You can either watch it happen or be part of it.

 

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Overview

Be part of the next generation of consumer and enterprise applications

What if you could learn and master some of the same techniques used in:

  • Self-driving cars
  • Microsoft Kinect
  • Google image search
  • Snapchat and Instagram filters

In this 7-hour course you will learn computer vision using Python 2.7 and develop skills in topics such as image filtering and processing, pattern recognition, machine learning and face detection.

These technologies are powering the next generation of consumer and enterprise applications.

From the Internet of Things, to advertising and gaming. We want you to get on board and be part of this revolution.

The course includes theory, lots of live coding and examples, and challenges for you to build your portfolio. Students can download the source code, projects and challenge solutions. Challenges include:

  • Receipt Segmenter – Find text in an image
  • Currency Counter – Count coins and dollar bills in an image
  • Multi-object Matching – Find Legend of Zelda rupees using a patter matching algorithm
  • Face Swap – App to swap the faces of two people

Python Computer Vision Banner-01

Complementing the application is the theory. We’ll discuss some of the mathematics and processes that happen under the hood to give you a better understanding of the concepts.

  • Colorspace conversions
  • Segmentation
  • Filters
  • Morphology
  • Edge Detection
  • Machine Learning
  • Face detection

At the end of the course, students will learn the fundamental computer vision techniques and be able to apply computer vision and image processing to their own images for a variety of cool tasks like building their own image filters, segmenting images, and even detecting faces in images!

How is machine learning covered in the course?

In this course, we will discuss some very elementary supervised machine learning topics such as support vector machines, decision trees, and Adaboost. These are just some elementary topics that lead to face detection using cascade classifiers, which use Adaboost and decision trees to determine if faces or eyes are present in an image. But to understand how cascade classifiers work, we first need to understand how Adaboost and decision trees work. However, both are techniques used in supervised learning. To understand supervised learning, we will use support vector machines.

Course author

Mohit Deshpande is a software developer author of several ZENVA courses in iOS and Android development (including Advanced Android App Development – From Padawan to Jedi).

But first and foremost, Mohit is a computer scientist. His field of research and main areas of expertise are Computer Vision and Artificial Intelligence.

Your Courses, Your Way

All of our project-based courses are designed to be flexible – you can access courses 24/7 to fit them around your schedule, and choose the learning materials that suit you best.

You can even download your course videos and watch them offline using the Zenva app, available on iOS and Android.

Learn from World-Class Instructors

Our course instructors participate in elite developer programs and have been recognized for their demonstrated excellence in development and teaching.

That way, you can be confident that you’re learning the most up-to-date content from industry experts.

Interactive Lessons with Codemurai

Our unlimited access package comes with free access to all of the courses in our mobile app, Codemurai!

Available on iOS and Android, it’s full of interactive programming lessons and exercises so you can continue your learning on the go.

Achieve Real Results

Our community of 500,000+ learners and developers have used the skills learned with us to publish their own games and websites, land their dream jobs, and even start their own businesses – and you have the potential to do the same!

Check out what our learners think below:

“I love the lectures, concise course objectives, and how they not only teach you enough to get started, but prepare you for the advanced stuff later down the road.”

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Mihir Patel

“With Zenva, I started learning a more accurate way to approach problems and develop solutions. It’s full of interesting topics that I love to learn in conjunction with my work.”

⭐⭐⭐⭐⭐

Monica Mancusi

“I chose Zenva because of the large selection of topics and the fact that I can choose what courses I want to take whenever I want to take them. That level of freedom is unique to Zenva.”

⭐⭐⭐⭐⭐

Craig Bickford

“The Unity courses at Zenva helped me achieve a level of comfort with game development that I didn’t think was possible. They give you the confidence to expand your skills, and were so easy to understand.”

⭐⭐⭐⭐⭐

Andrea Cardenas-Roeder

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Lessons

Requirements

  • Beginner-level experience with Python
  • Fundamental understanding of algebra
  • Course exercises are to be completed within a Linux environment
  • Installation of Linux virtual machine and necessary software is covered
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