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Master Computer Vision™ OpenCV4 in Python with Deep Learning Course

Master OpenCV4 like a pro while learning Dlib, Deep Learning Computer Vision (Keras, TensorFlow & Caffe) + 21 Projects!

What you’ll learn

Master Computer Vision™ OpenCV4 in Python with Deep Learning Course

  • Understand and use OpenCV4 in Python
  • How to use Deep Learning using Keras & TensorFlow in Python
  • Create Face Detectors & Recognizers and create your own advanced face swaps using DLIB
  • Object Detection, Tracking and Motion Analysis
  • Create Augmented Reality Apps
  • Programming skills such as basic Python and Numpy
  • How to use Computer Vision in executing cool startup ideas
  • Understand Neural and Convolutional Neural Networks
  • Learn to build simple Image Classifiers in Python
  • Learn to build an OCR Reader for Credit Cards
  • How to Perform Neural Style Transfer Using OpenCV
  • Learn how to do Multi-Object Detection in OpenCV (up to 90 Objects!) using SSDs (Single Shot Detector)
  • Learn how to convert black and white Images to color using Caffe
  • Build an Automatic Number (License) Plate Recognition (ALPR)
  • Learn the Basics of Computer Vision and Image Processing


  • Windows 10 or Ubuntu or a macOS system
  • A webcam to implement some of the mini-projects


Welcome to one of the most thorough and well-taught courses on OpenCV, where you’ll learn how to Master Computer Vision using the newest version of OpenCV4 in Python!

Computer Vision is an area of Artificial Intelligence that deals with how computer algorithms can decipher what they see in images! Master this incredible skill and be able to complete your University/College Projects, automate something at work, start developing your startup idea or gain the skills to become a high paying ($400-$1000 USD/Day) Computer Vision Engineer.


Last Updated Aug 2019, you will be learning:

  1. Key concepts of Computer Vision & OpenCV (using the newest version OpenCV4)
  2. Image manipulations (dozens of techniques!) such as transformations, cropping, blurring, thresholding, edge detection, and cropping.
  3. Segmentation of 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.
  4. Feature detection (SIFT, SURF, FAST, BRIEF & ORB) to do object detection.
  5. Object Detection for faces, people & cars.
  6. Extract facial landmarks for face analysis, applying filters and face swaps.
  7. Machine Learning in Computer Vision for handwritten digit recognition.
  8. Facial Recognition.
  9. Motion Analysis & Object Tracking.
  10. Computational photography techniques for Photo Restoration (eliminate marks, lines, creases, and smudges from old damaged photos).
  11. Deep Learning ( 3+ hours of Deep Learning with Keras in Python)
  12. Computer Vision Product and Startup Ideas
  13. Multi Object Detection (90 Object Types)
  14. Colorize Black & White Photos and Video (using Caffe)
  15. Neural Style Transfers – Apply the artistic style of Van Gogh, Picasso and others to any image even your webcam input
  16. Automatic Number-Plate Recognition (ALPR
  17. Credit Card Number Identification (Build your own OCR Classifier with PyTesseract)


You’ll also be implementing 21 awesome projects!


OpenCV Projects Include:

  1. Live Drawing Sketch using your webcam
  2. Identifying Shapes
  3. Counting Circles and Ellipses
  4. Finding Waldo
  5. Single Object Detectors using OpenCV
  6. Car and Pedestrian Detector using Cascade Classifiers
  7. Live Face Swapper (like MSQRD & Snapchat filters!!!)
  8. Yawn Detector and Counter
  9. Handwritten Digit Classification
  10. Facial Recognition
  11. Ball Tracking
  12. Photo-Restoration
  13. Automatic Number-Plate Recognition (ALPR)
  14. Neural Style Transfer Mini Project
  15. Multi-Object Detection in OpenCV (up to 90 Objects!) using SSD (Single Shot Detector)
  16. Colorize Black & White Photos and Video

Deep Learning Projects Include:

  1. Build a Handwritten Digit Classifier
  2. Learn how to build a Multi-Image Classifier
  3. Build a Cats vs Dogs Classifier
  4. Understand how to boost CNN performance using Data Augmentation
  5. Extract and Classify Credit Card Numbers

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!

This was my problem when learning Computer Vision and it became incredibly frustrating.

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.

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 get 3+ Hours of Deep Learning in Computer Vision using Keras, which includes:

  • A free Virtual Machine with all Deep Learning Python Libraries such as Keras and TensorFlow pre-installed
  • Detailed Explanations on Neural Networks and Convolutional Neural Networks
  • Understand how Keras works and how to use and create image datasets
  • Build a Handwritten Digit Classifier
  • Learn how to build a Multi-Image Classifier
  • Build a Cats vs Dogs Classifier
  • Understand how to boost CNN performance using Data Augmentation
  • Extract and Classify Credit Card Numbers

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

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