Computer Vision Course: Learn Computer Vision with Python from Basics to Projects
Learn Computer Vision from scratch. Understand digital images, pixels, color spaces, and image processing. Master OpenCV, edge detection, image segmentation, OCR, face detection, object detection, video processing, and CNNs for visual data. Build practical Computer Vision projects using Python. Free course for beginners and intermediate learners.
What's Inside Every Lesson?
Every topic is designed so complete beginners can follow along step by step
Interactive Code Editor
Write & run real code in the browser - no setup needed
Practice After Every Topic
Hands-on exercises reinforce what you just learned
Q&A Flip Cards
Common questions answered with simple explanations
Module Quizzes
Test your knowledge before moving to the next module
Mini Projects
Build real apps - a calculator, to-do list, and more
Visual Diagrams
Concepts explained with clear, annotated diagrams
Who This Course Is For
Designed for students, beginners, Python learners, web developers, software developers, QA testers, robotics beginners, and anyone interested in how computers work with images and video. No Computer Vision background needed.
Prerequisites
No prior Computer Vision knowledge required. Basic computer knowledge is enough to start. Basic Python is helpful but not mandatory for understanding the core concepts.
First published October 2024 · Updated 2026
What You'll Learn
- What a digital image is and how pixels store visual information
- How to read, display, save, and modify images using Python and OpenCV
- How to resize, crop, rotate, flip, and adjust brightness and contrast
- What color channels are and how RGB, BGR, and HSV color spaces work
- How to detect colors and apply image filters including Gaussian blur
- How to detect edges, shapes, contours, lines, and circles in images
- What image segmentation is and how to separate objects from backgrounds
- How OCR works and how to extract text from images using Python
- How to detect faces in images and video using OpenCV
- What object detection is and how bounding boxes and confidence scores work
- How to process video files and live camera input frame by frame
- How to detect motion and track objects in video streams
- What a Convolutional Neural Network is and how convolution works visually
- How to run image classification using a pretrained CNN model
- How to plan and build practical Computer Vision projects
Career Opportunities
Course Modules Overview
Computer Vision Fundamentals
14 topics
Working with Images Using Python
14 topics
Image Processing Basics
14 topics
Colors, Filters and Image Transformations
14 topics
Edge, Shape and Feature Detection
14 topics
Image Segmentation and Object Extraction
14 topics
Optical Character Recognition
14 topics
Face Detection and Face Analysis
14 topics
Object Detection
14 topics
Computer Vision with Video and Cameras
14 topics
Convolutional Neural Networks for Computer Vision
14 topics
Real-World Computer Vision Projects and Career
14 topics
Complete all 12 modules to unlock your course completion certificate
Course Curriculum
12 comprehensive modules covering everything from basics to advanced topics
Computer Vision Fundamentals
Start your Computer Vision journey. Learn what a digital image really is, how pixels work, what color channels are, and where Computer Vision is used in the real world.
Working with Images Using Python
Set up your Python environment and start working with images. Learn OpenCV, NumPy arrays for images, and how to read, display, modify, and save image files.
Image Processing Basics
Apply core image processing operations using OpenCV. Resize, crop, rotate, flip, adjust brightness and contrast, convert to grayscale, and apply blur and threshold transformations.
Colors, Filters and Image Transformations
Go deeper into color spaces including RGB, BGR, and HSV. Learn to detect and mask colors, apply blur filters, remove noise, and use edge-preserving filters for practical image tasks.
Edge, Shape and Feature Detection
Detect edges, shapes, and features in images. Learn Canny edge detection, contour finding, line and circle detection, and image feature concepts using OpenCV.
Image Segmentation and Object Extraction
Learn how to separate regions of an image using segmentation techniques. Apply threshold-based and color-based segmentation, masking, and contour-based object extraction.
Optical Character Recognition
Understand how OCR converts text in images into machine-readable text. Prepare images for OCR, use Tesseract with Python, and build a practical OCR document reader.
Face Detection and Face Analysis
Learn how to detect faces in images and video using Haar cascades and OpenCV. Understand the important difference between face detection and face recognition, and apply responsible practices.
Object Detection
Learn how object detection works including bounding boxes, labels, and confidence scores. Understand YOLO conceptually and run object detection using a pretrained model in Python.
Computer Vision with Video and Cameras
Work with video files and live camera input using OpenCV. Process frames in real time, detect motion, apply background subtraction, and track objects in video streams.
Convolutional Neural Networks for Computer Vision
Understand how Convolutional Neural Networks work for image recognition. Learn convolution, filters, feature maps, pooling, and how CNNs classify images using visual patterns.
Real-World Computer Vision Projects and Career
Plan, build, test, and document a complete Computer Vision project. Understand real-world challenges such as lighting, occlusion, and camera angles. Explore Computer Vision career opportunities.
Your Learning Roadmap
Follow this structured path - from first concepts to production-ready mastery
Understand digital images, pixels, color channels, and start reading and modifying images with Python and OpenCV
Apply image processing, edge detection, segmentation, OCR, face detection, object detection, and video processing
Build complete Computer Vision applications, understand CNNs visually, and apply skills to real-world projects
Understand digital images, pixels, color channels, and start reading and modifying images with Python and OpenCV
Apply image processing, edge detection, segmentation, OCR, face detection, object detection, and video processing
Build complete Computer Vision applications, understand CNNs visually, and apply skills to real-world projects
Tools & Technologies
Essential tools you'll master during this course
Python
Primary language for all Computer Vision code examples and projects
OpenCV
The most widely used open-source library for Computer Vision tasks
NumPy
Handles image data as multi-dimensional arrays for processing
Pillow
Python Imaging Library for reading and manipulating image files
Tesseract
Open-source OCR engine for extracting text from images
Jupyter Notebook
Interactive environment for running and visualizing Computer Vision experiments
Ready to Start Learning?
Begin your journey with Module 1 and build your skills step by step. Completely free, no registration required.
Start Learning Computer Vision Free