This Computer Vision full course provides a comprehensive introduction to modern image processing and artificial intelligence techniques used in visual data analysis. It is designed for beginners and intermediate learners who want to build real-world computer vision applications using Python.
The course begins with foundational concepts in OpenCV, including image processing, color detection using HSV color space, and basic computer vision operations. It then introduces face detection and image manipulation techniques such as blurring and masking.
Learners explore Optical Character Recognition (OCR) using tools like Tesseract, EasyOCR, and AWS Textract, comparing their performance in real-world scenarios. The course also covers image classification using Scikit-learn and feature extraction techniques for visual data.
Advanced topics include emotion detection, sign language recognition, and landmark-based analysis using MediaPipe. The course also demonstrates object detection using YOLO models, including YOLOv8 and YOLOv10, along with segmentation and pose estimation.
Practical projects such as parking space detection and end-to-end computer vision pipelines help learners apply concepts in real-world scenarios. Additionally, learners build web applications using Streamlit to deploy AI models.
By the end of this course, learners gain strong hands-on experience in computer vision, from basic image processing to advanced deep learning-based object detection systems.
