Data Science

Python for Computer Vision with OpenCV and Deep Learning course  is the finest resource for learning how to programme in Python for computer vision. Students will learn  how to analyse image and video data using Python and the OpenCV (Open Computer Vision) package.

The course will begin with an introduction to the NumPy library, numerical processing, and opening and manipulating images. The OpenCV library will then be used to open and manipulate basic image data after that. Then, Instructor will teach how to edit pictures and use a range of effects, such as gradients, thresholds, colour blending, and more. Currently, udemy is offering Python for Computer Vision with OpenCV and Deep Learning for up to 85% off i.e. INR 455 (INR 2,799)

Who all can opt for this course?

  • Python programmers with a deep learning and computer vision interest
  • Python beginners should not take this course

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 455 (INR 2,79985% off
Duration14 Hours
Student Enrollment49,915 students
InstructorJose Portilla
Topics CoveredNumPy, Color mapping, Deep learning with keras, Face detection, watershed algorithm and more
Course LevelIntermediate
Total Student Reviews9,195

Learning Outcomes

  • learn the fundamentals of NumPy
  • Use NumPy to manipulate and open images
  • To work with picture files, use OpenCV
  • To add shapes to photos and videos, use Python and OpenCV
  • Use OpenCV to manipulate images, including morphological operations, thresholding, blurring, and smoothing
  • Use OpenCV to create colour histograms
  • Python and OpenCV are used to open and stream video
  • Using OpenCV and Python, detect objects using corner, edge, and grid detection methods
  • Make software that can detect faces
  • Use the Watershed Algorithm to segment images
  • Object tracking in video
  • Create image classifiers using Python and deep learning
  • To train on your own unique photos, use Python, Keras, and Tensorflow

Course Content

S.No.Module (Duration)Topics
1.Course Overview and Introduction (23 minutes)Course Overview
FAQ – Frequently Asked Questions
Course Curriculum Overview
Getting Set-Up for the Course Content
2.NumPy and Image Basics (47 minutes)Introduction to Numpy and Image Section
NumPy Arrays
What is an image?
Images and NumPy
NumPy and Image Assessment Test
NumPy and Image Assessment Test – Solutions
3.Image Basics with OpenCV (01 hour 32 minutes)Introduction to Images and OpenCV Basics
Opening Image files in a notebook
Opening Image files with OpenCV
Drawing on Images – Part One – Basic Shapes
Drawing on Images Part Two – Text and Polygons
Direct Drawing on Images with a mouse – Part One
Direct Drawing on Images with a mouse – Part Two
Direct Drawing on Images with a mouse – Part Three
Image Basics Assessment
Image Basics Assessment Solutions
4.Image Processing (02 hours 36 minutes)Introduction to Image Processing
Color Mappings
Blending and Pasting Images
Blending and Pasting Images Part Two – Masks
Image Thresholding
Blurring and Smoothing
Blurring and Smoothing – Part Two
Morphological Operators
Histograms – Part One
Histograms – Part Two – Histogram Eqaulization
Histograms Part Three – Histogram Equalization
Image Processing Assessment
Image Processing Assessment Solutions
5.Video Basics with Python and OpenCV (45 minutes)Introduction to Video Basics
Connecting to Camera
Using Video Files
Drawing on Live Camera
Video Basics Assessment
Video Basics Assessment Solutions
6.Object Detection with OpenCV and Python (03 hours 05 minutes)Introduction to Object Detection
Template Matching
Corner Detection – Part One – Harris Corner Detection
Corner Detection – Part Two – Shi-Tomasi Detection
Edge Detection
Grid Detection
Contour Detection
Feature Matching – Part One
Feature Matching – Part Two
Watershed Algorithm – Part One
Watershed Algorithm – Part Two
Custom Seeds with Watershed Algorithm
Introduction to Face Detection
Face Detection with OpenCV
Detection Assessment
Detection Assessment Solutions
7.Object Tracking (01 hour 09 minutes)Introduction to Object Tracking
Optical Flow
Optical Flow Coding with OpenCV – Part One
Optical Flow Coding with OpenCV – Part Two
MeanShift and CamShift Tracking Theory
MeanShift and CamShift Tracking with OpenCV
Overview of various Tracking API Methods
Tracking APIs with OpenCV
8.Deep Learning for Computer Vision (03 hours 03 minutes)Introduction to Deep Learning for Computer Vision
Machine Learning Basics
Understanding Classification Metrics
Introduction to Deep Learning Topics
Understanding a Neuron
Understanding a Neural Network
Cost Functions
Gradient Descent and Back Propagation
Keras Basics
MNIST Data Overview
Convolutional Neural Networks Overview – Part One
Convolutional Neural Networks Overview – Part Two
Keras Convolutional Neural Networks with MNIST
Keras Convolutional Neural Networks with CIFAR-10
Deep Learning on Custom Images – Part One
Deep Learning on Custom Images – Part Two
Deep Learning and Convolutional Neural Networks Assessment
Deep Learning and Convolutional Neural Networks Assessment Solutions
Introduction to YOLO v3
YOLO Weights Download
YOLO v3 with Python
9.Capstone Project (41 minutes)Introduction to CapStone Project
Capstone Part One – Variables and Background function
Capstone Part Two – Segmentation
Capstone Part Three – Counting and ConvexHull
Capstone Part Four – Bringing it all together

Resources Required

  • Basic knowledge of Python is required
  • Ubuntu, Windows 10, or Mac OS
  • Must have Computer Install Permissions
  • If you want to learn about the video streaming material, use a webcam

Featured Review

Mah Kaiquan (5/5) : Good introduction to OpenCV, CNN and image processing concepts with lecture videos, readings and Jupyter notebook exercises. Jose and different teaching assistants such as Yau were helpful in answering conceptual and code-related questions I had usually within a day. The quick response was refreshing for an online course.


  • Lei Deng (5/5) : He is always to the point and provides an excellent context of how the code fits in.
  • Helge Plehn (5/5) : I had some difficulties with the Jupyter notebooks at the start, but after that everything worked perfectly.
  • Saurav Banerjee (5/5) : This is an awesome course for a beginner like me in the Computer Vision field.
  • Shalini Shukla (5/5) : I’m really happy with the content of the course which is well explained!


  • Anonymized User (2/5) : The linux .yml file is broken and seemingly has been for some time.
  • Ça?atay (2/5) : Also I hate how you are told to use certain parameters without any explanation on why we used them.
  • Vy Dinh (1/5) : After few hours, I decided to manually install them without Conda
  • Chirag Rao (1/5) : The Capstone Project is a mess , he hardly explains lines properly and he thinks that we already have a lot of experience with OpenCV.

About the Author

The instructor of this course is Jose Portilla who is a Head of Data Science at Pierian Training. With 4.6 Instructor Rating and 1,022,766 Reviews on Udemy, he/she offers 60 Courses and has taught 3,292,376 Students so far.

  • Jose Marcial Portilla holds degrees in mechanical engineering from Santa Clara University (BS and MS), and he has years of experience working as a qualified instructor and trainer for Python programming, machine learning, and data science
  • He has written articles and received patents in a number of disciplines, including data science, materials science, and microfluidics
  • He has acquired a set of abilities for data analysis throughout the course of his career, and he wants to combine both his teaching and data science knowledge to educate others the power of programming, how to analyse data, and how to display the data in attractive visualisations
  • He currently serves as the Head of Data Science for Pierian Training, where he trains people at prestigious organisations like General Electric, Cigna, The New York Times, Credit Suisse, McKinsey, and others in data science and python programming on-site
  • Please click the website link to learn more about the available training options

Comparison Table

ParametersPython for Computer Vision with OpenCV and Deep LearningNLP – Natural Language Processing with PythonInteractive Python Dashboards with Plotly and Dash
OffersINR 455 (INR 2,799) 85% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration14 hours11.5 hours9.5 hours
Rating4.5 /54.6 /54.6 /5
Student Enrollments49,91068,42644,688
InstructorsJose PortillaJose PortillaJose Portilla
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