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IT & ENGINEERING Advanced

Computer Vision with AI: From Detection to Multimodal Understanding

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A premium, complete and advanced course on modern computer vision, updated for 2026. You will learn how images are represented and preprocessed with OpenCV, how convolutional neural networks classify images, how object detection works with YOLO and DETR, semantic and instance segmentation, the Segment Anything family (SAM 3.1) and promptable segmentation, Vision Transformers (ViT), multimodal vision-language models (GPT-5.5 vision, Gemini 3.1 Pro, Claude vision), OCR and document understanding, face detection and recognition with a rigorous legal and ethical framework (GDPR Article 9, EU AI Act biometric restrictions), video analysis and object tracking, the basics of generative vision with diffusion models, and production deployment and edge optimization. Every module includes real, runnable Python, PyTorch and OpenCV code, a strong focus on legal compliance and responsible use of biometric systems, and a comprehensive final assessment.

10 modules
26 lessons
~6h duration
v1.0 version
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Hands-on exercises Real scenarios and practical exercises directly on the platform, with instant feedback
Progress & analytics A personal dashboard with statistics, streaks, scores and structured learning paths
Interactive AI quizzes Questions generated by AI and adapted to your level, with detailed explanations
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What you will learn

Practical skills you gain by completing this course

Foundations of Computer Vision in 2026
Image Preprocessing with OpenCV
Convolutional Neural Networks for Classification
Object Detection
Segmentation and Segment Anything
Vision Transformers and Multimodal Models
OCR, Documents, Faces, and the Law
Video Analysis, Tracking, and Generative Vision
Evaluation, Deployment, and Optimization
Final Quiz — Computer Vision with AI

Who it is for

Developers Software engineers Solution architects CTOs / Tech Leads Data Scientists ML Engineers DevOps Engineers

Recommended level

Advanced

Assumes hands-on experience with AI and complex scenarios.

Updates

Regular

Content updated regularly with the latest practices from the industry.

Category

IT & Engineering

A technical course for IT professionals — available with individual course access or the IT Pro / All Access bundle.

Advanced level

Hands-on experience required

Assumes practical experience with AI. Covers complex scenarios and advanced strategies.

Always up to date

Up-to-date content

The course is updated regularly with the latest information, tools and practices from the industry.

Practical and applied

26 lessons with real examples

Each lesson includes practical scenarios, actionable checklists and quizzes to check your understanding.

Curriculum

10 modules, 26 lessons — structured to learn step by step.

10 modules
26 lessons
~6h of content
Interactive quizzes
Free preview available What Computer Vision Is and the 2026 Landscape
Read the preview
1 Free preview lesson What Computer Vision Is and the 2026 Landscape
Read the preview
2 How Images Are Represented: Pixels, Channels, and Color Spaces
13 min
3 The Modern CV Stack: OpenCV, PyTorch, and the Ecosystem
12 min
1 Loading, Displaying, and Geometric Transformations
12 min
2 Filtering, Edges, and Classical Feature Detection
13 min
3 Data Augmentation and Preprocessing Pipelines
12 min
1 Convolutional Neural Networks Explained
14 min
2 Building and Training a CNN Classifier in PyTorch
14 min
3 Transfer Learning and Fine-Tuning Pretrained Backbones
13 min
1 Object Detection Fundamentals: Boxes, IoU, NMS
14 min
2 YOLO in Practice
13 min
3 DETR and Transformer-Based Detection
13 min
1 Semantic, Instance, and Panoptic Segmentation
13 min
2 Segment Anything: Promptable Segmentation with SAM 3.1
13 min
3 Training and Evaluating Segmentation Models
12 min
1 Vision Transformers (ViT) Explained
13 min
2 Vision-Language Models: CLIP to Modern VLMs
14 min
3 Using Vision-Language Models in Practice
13 min
1 OCR and Document Understanding
13 min
2 Face Detection and Recognition: Technology and Legal Duties
14 min
3 Legal and Ethical Computer Vision
13 min
1 Video Analysis and Object Tracking
13 min
2 Generative Vision: Diffusion Models Basics
13 min
1 Evaluating Computer Vision Systems
13 min
2 Deployment and Edge Optimization
14 min
1 Final Assessment — Computer Vision with AI
40 min
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