Practical Computer Vision with YOLOv8 and YOLO-NAS

Computer vision is transforming industries, but getting started with state-of-the-art object detection can feel overwhelming. This course simplifies the process, guiding you from foundational concepts to deploying highly accurate models. By following our clear, text-based lessons, you will understand the core mechanics of YOLOv8 and YOLO-NAS, learn how to prepare custom datasets, and write clean Python code to run inference on real-world scenarios. What you'll learn: - Understand the core architectures and differences between YOLOv8 and YOLO-NAS - Prepare and annotate custom image datasets using modern workflow best practices - Train object detection models on custom data using Python and virtual environments - Evaluate model performance using key metrics like precision, recall, and mAP - Deploy trained models for inference using optimized runtime environments - Apply post-processing techniques to refine model predictions and handle edge cases The course begins with foundational computer vision terminology and environment setup before progressing to hands-on training workflows and practical deployment strategies. You will read through clear code explanations and structured text exercises designed to build real-world confidence. This course is designed for aspiring developers, data enthusiasts, and beginners looking to enter the field of computer vision. No prior machine learning experience is required. Start your journey into modern computer vision and master object detection today.