
Beginner-friendly introduction to AI concepts, Python, and OpenCV, covering face, hand, and body detection techniques for absolute beginners.

Explore state-of-the-art object detection systems, including two-stage, one-stage, and transformer-based approaches. Learn to upgrade detection pipelines and tackle key challenges in 2D and 3D object detection.

Explore using pgvector and PostgreSQL to automate finding photos of yourself at events. Learn to extract faces, calculate embeddings, and query image data efficiently.

Learn to build a text-to-image AI app using Stable Diffusion, Docker, and Flask. Create high-res images from text prompts, explore Docker Init, CUDA, FreeU, and more in this beginner-friendly Python tutorial.

Autonomous robot using OpenCV and Arduino to track and follow faces, with real-time adjustments based on face position and size for movement control.

Learn face recognition using OpenCV and Python. Covers importing images, setting up environments, finding faces, encoding, and labeling. Practical guide for implementing facial recognition in computer vision projects.

Learn to build a computer vision system that accurately counts and identifies money and coins using OpenCV, covering setup, image processing, object detection, and color analysis.

Learn to create a virtual coffee machine interface using computer vision techniques, including hand detection, selection modes, and interactive elements.

Learn to use OpenCV with Python for computer vision tasks. Process images and live video, create bounding boxes, and trigger events based on real-time input. Run locally for privacy on various systems.

Comprehensive Python-based OpenCV tutorial covering installation, core principles, and practical projects. Ideal for beginners, focuses on hands-on implementation of computer vision techniques for real-world problem-solving.

Comprehensive Python-based OpenCV tutorial covering fundamentals to advanced applications, including face detection, object tracking, and image classification. Ideal for aspiring computer vision professionals.

Learn to build a real-time weed detection model using YOLOR and OpenCV in Python. Extract weed coordinates and implement computer vision techniques for agricultural applications.

Learn to create a Bernie Sanders detector using YOLOv4/v5, OpenCV, and Roboflow. Build datasets, train models, and implement real-time detection on various platforms.

Build a real-time mask detection access control system using Raspberry Pi, OpenCV AI Kit, and a stepper motor. Learn hardware setup, coding, and dataset annotation with Roboflow for this practical AI application.

Learn to detect chess pieces using YOLOv5 and OpenCV in Python. Explore object detection techniques, train a custom model, and implement computer vision for chess analysis.

Build a real-time social distancing detection system using Raspberry Pi and OpenCV AI Kit. Learn to implement computer vision for monitoring and alerting, with depth capability and high performance on an embedded platform.

Build a real-time AI surveillance camera using Raspberry Pi and OpenCV AI Kit. Detect objects, people, and packages with MobileNet SSD at 30 FPS. Includes hardware setup, coding, and Roboflow integration for custom object detection.

Learn to create a Python-based visual inspection system that automatically detects and identifies surface defects using AI and computer vision techniques.

Learn to build an AI-powered object detection system using Python, OpenCV, and YOLO, covering essential tools, dataset preparation, and model training techniques.

Comprehensive exploration of AI, covering neural networks, deep learning, computer vision, NLP, and generative AI. Includes hands-on demos and real-world applications for beginners to advanced learners.

Master real-time vehicle detection and tracking using YOLO11, implementing class-wise counting with BoT-SORT and ByteTrack algorithms for traffic analysis and smart city applications.