Deep learning

The ‘Master Computer Vision™ OpenCV4 in Python with Deep Learning Course’ is a hands-on training program where students will learn how to master computer vision using the latest version of OpenCV4 in Python. Computer vision is a part of the AI that focuses on ways that computer-based algorithms decipher images.

The course covers key concepts of computer vision and OpenCV4, image manipulation, feature detection, object detection, facial recognition, motion analysis, object tracking, etc. Students will also work on a total of 21 projects based on applications of OpenCV4 and Deep Learning. The course is usually available for INR 2,499 on Udemy but students can click on the link and get the ‘Master Computer Vision™ OpenCV4 in Python with Deep Learning Course’ for INR 449.

Who all can opt for this course?

  • Beginners curious to learn about computer vision
  • College students looking for an introductory course on computer vision
  • Anybody interested to learn about computer vision using deep learning
  • Entrepreneurs looking to implement business ideas for computer vision
  • Making a fantastic computer vision prototype as a hobby
  • Software engineers and developers who desire to hone their computer vision skills

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 2,49982% off
Duration10.5 hours
Student Enrollment21,135 students
InstructorRajeev D. Ratan
Topics CoveredKey concepts of computer vision, OpenCV4, image manipulation, image segmentation, facial recognition, deep learning, computational photography, etc.
Course LevelBeginner
Total Student Reviews3,597

Learning Outcomes

  • Learn and use OpenCV4 in Python
  • How to utilise deep learning using Keras and TensorFlow in Python
  • With DLIB, make your own sophisticated face swaps and face detectors
  • Motion analysis, object tracking, and object detection
  • Make apps for augmented reality
  • Fundamentals of Numpy and Python
  • How to implement innovative company concepts using computer vision
  • Learn about neural networks and convolutional networks
  • Learn how to create straightforward Python image classifiers
  • Build a credit card OCR reader by learning how to do it
  • Learn to use OpenCV for Neural Style Transfer
  • Discover how to use SSDs for multi-object detection in OpenCV (up to 90 Objects!) (Single Shot Detector)
  • Learn how to use Caffe to transform grayscale images into colour
  • Develop an automatic number plate (licence plate) recognition system (ALPR)
  • Study the fundamentals of image processing and computer vision

Course Content

S.No.Module (Duration)Topics
1.Course Introduction and Setup (30 minutes)Introduction
Introduction to Computer Vision and OpenCV
About this course
READ THIS – Guide to installing and setting up your OpenCV4.0.1 Virtual Machine
Recommended – Setup your OpenCV4.0.1 Virtual Machine
Installation of OpenCV & Python on Windows
Installation of OpenCV & Python on Mac
Installation of OpenCV & Python on Linux
Set up course materials (DOWNLOAD LINK BELOW) – Not needed if using the new VM
2.Basics of Computer Vision and OpenCV (43 minutes)What are Images?
How are Images Formed?
Storing Images on Computers
Getting Started with OpenCV – A Brief OpenCV Intro
Grayscaling – Converting Color Images To Shades of Gray
Understanding Color Spaces – The Many Ways Color Images Are Stored Digitally
Histogram representation of Images – Visualizing the Components of Images
Creating Images & Drawing on Images – Make Squares, Circles, Polygons & Add Text
3.Image Manipulations & Processing (01 hour 01 minutes)Transformations, Affine And Non-Affine – The Many Ways We Can Change Images
Image Translations – Moving Images Up, Down. Left And Right
Rotations – How To Spin Your Image Around And Do Horizontal Flipping
Scaling, Re-sizing and Interpolations – Understand How Re-Sizing Affects Quality
Image Pyramids – Another Way of Re-Sizing
Cropping – Cut Out The Image The Regions You Want or Don’t Want
Arithmetic Operations – Brightening and Darkening Images
Bitwise Operations – How Image Masking Works
Blurring – The Many Ways We Can Blur Images & Why It’s Important
Sharpening – Reverse Your Images Blurs
Thresholding (Binarization) – Making Certain Images Areas Black or White
Dilation, Erosion, Opening/Closing – Importance of Thickening/Thinning Lines
Edge Detection using Image Gradients & Canny Edge Detection
Perspective & Affine Transforms – Take An Off Angle Shot & Make It Look Top Down
Mini Project 1 – Live Sketch App – Turn your Webcam Feed Into A Pencil Drawing
4.Image Segmentation & Contours (57 minutes)Segmentation and Contours – Extract Defined Shapes In Your Image
Sorting Contours – Sort Those Shapes By Size
Approximating Contours & Finding Their Convex Hull – Clean Up Messy Contours
Matching Contour Shapes – Match Shapes In Images Even When Distorted
Mini Project 2 – Identify Shapes (Square, Rectangle, Circle, Triangle & Stars)
Line Detection – Detect Straight Lines E.g. The Lines On A Sudoku Game
Circle Detection
Blob Detection – Detect The Center of Flowers
Mini Project 3 – Counting Circles and Ellipses
5.Object Detection in OpenCV (50 minutes)Object Detection Overview
Mini Project # 4 – Finding Waldo (Quickly Find A Specific Pattern In An Image)
Feature Description Theory – How We Digitally Represent Objects
Finding Corners – Why Corners In Images Are Important to Object Detection
SIFT, SURF, FAST, BRIEF & ORB – Learn The Different Ways To Get Image Features
Mini Project 5 – Object Detection – Detect A Specific Object Using Your Webcam
Histogram of Oriented Gradients – Another Novel Way Of Representing Images
6.Object Detection – Build a Face, People and Car/Vehicle Detectors (22 minutes)HAAR Cascade Classifiers – Learn How Classifiers Work And Why They’re Amazing
Face and Eye Detection – Detect Human Faces and Eyes In Any Image
Mini Project 6 – Car and Pedestrian Detection in Videos
7.Augmented Reality (AR) – Facial Landmark Identification (Face Swaps) (35 minutes)Face Analysis and Filtering – Identify Face Outline, Lips, Eyes Even Eyebrows
Merging Faces (Face Swaps) – Combine Two Faces For Fun & Sometimes Scary Results
Mini Project 7 – Live Face Swapper (like MSQRD & Snapchat filters!!!)
Mini Project 8 – Yawn Detector and Counter
8.Simple Machine Learning using OpenCV (41 minutes)Machine Learning Overview – What Is It & Why It’s Important to Computer Vision
Mini Project 9 – Handwritten Digit Classification
Mini Project # 10 – Facial Recognition – Make Your Computer Recognize You
9.Object Tracking & Motion Analysis (34 minutes)Filtering by Color
Background Subtraction and Foreground Subtraction
Using Meanshift for Object Tracking
Using CAMshift for Object Tracking
Optical Flow – Track Moving Objects In Videos
Mini Project # 11 – Ball Tracking
10.Computational Photography & Make a License Plate Reader (06 minutes)Mini Project # 12 – Photo-Restoration
Mini Project # 13 – Automatic Number-Plate Recognition (ALPR)
11.Conclusion (09 minutes)Course Summary and how to become an Expert
Latest Advances, 12 Startup Ideas & Implementing Computer VIsion in Mobile Apps
12.BONUS – Deep Learning Computer Vision 1 – Setup a Deep Learning Virtual Machine (19 minutes)Setup your Deep Learning Virtual Machine
Intro to Handwritten Digit Classification (MNIST)
Intro to Multiple Image Classification (CIFAR10)
13.BONUS – Deep Learning Computer Vision 2 – Introduction to Neural Networks (01 hour 34 minutes)Neural Networks Chapter Overview
Machine Learning Overview
Neural Networks Explained
Forward Propagation
Activation Functions
Training Part 1 – Loss Functions
Training Part 2 – Backpropagation and Gradient Descent
Backpropagation & Learning Rates – A Worked Example
Regularization, Overfitting, Generalization and Test Datasets
Epochs, Iterations and Batch Sizes
Measuring Performance and the Confusion Matrix
Review and Best Practices
14.BONUS – Deep Learning Computer Vision 3 – Convolutional Neural Networks (CNNs) (42 minutes)Convolutional Neural Networks Chapter Overview
Introduction to Convolutional Neural Networks (CNNs)
Convolutions & Image Features
Depth, Stride and Padding
The Fully Connected Layer
Training CNNs
Designing Your Own CNN
15.BONUS – Deep Learning Computer Vision 4 – Build CNNs in Python using Keras (52 minutes)Introduction to Keras & Tensorflow
Building a CNN in Keras
Building a Handwriting Recognition CNN
Loading Our Data
Getting our data in ‘Shape’
Hot One Encoding
Building & Compiling Our Model
Training Our Classifier
Plotting Loss and Accuracy Charts
Saving and Loading Your Model
Displaying Your Model Visually
Building a Simple Image Classifier using CIFAR10
16.BONUS – Deep Learning Computer Vision 5 – Build a Cats vs Dogs Classifier (25 minutes)Data Augmentation Chapter Overview
Splitting Data into Test and Training Datasets
Train a Cats vs. Dogs Classifier
Boosting Accuracy with Data Augmentation
Types of Data Augmentation
17.BONUS – Build a Credit Card Number Reader (12 minutes)Step 1 – Creating a Credit Card Number Dataset
Step 2 – Training Our Model
Step 3 – Extracting A Credit Card from the Background
Step 4 – Use our Model to Identify the Digits & Display it onto our Credit Card
18.BONUS – Neural Style Transfer with OpenCV (01 minute)Perform Neural Style Transfer Using OpenCV4
19.BONUS – Object Detection – Use SSDs (Single Shot Detector) for Detecting Objects (03 minutes)Using an SSD In OpenCV
20.BONUS – Colorize Black and White Images (01 minute)Colorizing Black and White Images Using Caffe

Resources Required

  • Basic programming knowledge is helpful but is not absolutely necessary
  • A macOS, Windows 10, or Ubuntu computer
  • A webcam for some of the smaller tasks

Featured Review

Alexander Baker (5/5): Concise, and informative. Excellent course to get a handle on the computer vision capabilities of Python. (As the description reads) it’s not a from scratch course though. Learn the basics of machine learning and Python in a different course before this one.


  • Abhijit Debnath (5/5): This is an excellent course for someone to get started with Computer Vision using Python OpenCV.
  • Pedro Sousa (5/5): Giving us the source code in Jupyter Notebooks is just awesome!
  • Andrew Norris (5/5): 5++ starts! One of the best investments I’ve ever made here at Udemy.
  • Darshan Iyer (5/5): Rajeev did a brilliant job in putting a great open CV course together.


  • Abdulsamad Gobir (1/5): I am really disappointed at why I have to go to another site to get instructions to install and even at that the pyimage search sites do use anaconda in their installation process.
  • Alain Seux-Bos (1/5): And a guy that ask to run 2 times the code to see graphs from matplotlib beacuse he don’t know #matplotlib inline is a really bad teacher.
  • Stevaras (2/5): Even wrong comments in some parts of the code and errors in presentations ( like erosion/dilation ) 6) Errors when trying to execute his code.
  • Stephan Bernstorf (1/5): the instructor just talks about random image manipulation topics without any context, provides VM that doesnt seem to work.

About the Author

The instructor of this course is Rajeev D. Ratan who is a Data Scientist, Computer Vision Expert & Electrical Engineer. With a 4.4 instructor rating and 9,177 reviews on Udemy, he offers 7 courses and has taught 61,761 students so far.

  • He graduated from the University of Edinburgh with a BSc in Computer & Electrical Engineering and an MSc in Artificial Intelligence degree. He gained in-depth expertise in machine learning, computer vision, and intelligent robotics.
  • Rajiv was a member of a team that won a robotics competition at the University of Edinburgh.
  • His research on using data-driven methods for probabilistic stochastic modelling for public transportation has been published.
  • In order to use deep learning in education, he founded his own computer vision startup.
  • Since then, he has worked with two other companies in the computer vision space as well as a large international company in data science.
  • He has spent over 8 years working for two of the biggest telecom companies in the Caribbean, where he developed skills in technical staff management and the deployment of challenging telecom projects.

Comparison Table

ParametersMaster Computer Vision™ OpenCV4 in Python with Deep LearningArtificial Intelligence: Reinforcement Learning in PythonAdvanced AI: Deep Reinforcement Learning in Python
OffersINR 449 (INR 2,499) 82% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration10.5 hours14.5 hours10.5 hours
Rating4.2/54.8 /54.6 /5
Student Enrollments21,13543,64736,737
InstructorsRajeev D. RatanLazy Programmer TeamLazy Programmer Team
Register HereApply Now!Apply Now!Apply Now!

Leave feedback about this

  • Rating