YOLOv11 Object Detection: Computer Vision with Python

About this course

Want to build computer vision systems that detect and track objects in images and video, and not just read about them? This course takes you from Python and OpenCV basics to training, tracking and deploying custom YOLO models.

Who this course is for:

• Developers and engineers who want hands-on, end-to-end computer vision skills
• Students moving from theory into practical deep learning projects
• Technical professionals exploring how vision AI could apply in their own field

What you'll build and practise:

• Image processing demos in OpenCV: transformations, filtering, enhancement and edge detection
• Object detection with YOLO on practical examples
• Custom YOLO models trained on your own datasets with Roboflow
• Instance segmentation and multi-object tracking with BoT-SORT and ByteTrackComplete example projects that bring every concept together

What's inside:

• Foundations: what computer vision is used for, and an introduction to the YOLO algorithm

Setup and image processing: configuring your Python environment, installing OpenCV, and learning the core techniques every vision pipeline relies onObject detection with YOLO: how modern detection pipelines work and how to apply them

Roboflow: managing datasets, integrating with deep learning frameworks and cloud services, automating workflows, and training and evaluating custom modelsYOLO architecture: the backbone network, detection layers and loss functions, so you understand why the model behaves as it does before you train itCustom training: hands-on training of YOLO models on custom datasets

Deployment: exporting trained models, integrating them into applications and monitoring their performance

Advanced demos: instance segmentation, object tracking and full example projects

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