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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, instance and panoptic segmentation, the Segment Anything family (SAM 3.1) and promptable segmentation, Vision Transformers (ViT), self-supervised vision foundation models, multimodal vision-language models (GPT-5.6 Sol vision, Gemini 3.1 Pro, Claude vision), OCR and document understanding, face detection and recognition under a rigorous legal and ethical framework (GDPR Article 9, EU AI Act biometric restrictions), video analysis and object tracking, pose and 3D depth estimation, the basics of generative vision with diffusion models, dataset construction and auditing, evaluation, production deployment, edge optimization and MLOps monitoring. 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
31 lessons
~25h 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 and Datasets 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, Tracking, 3D, and Generative Vision
Evaluation, Deployment, and Production MLOps
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

Last update: Aug 8, 2026. Content kept up to date.

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

Last update: Aug 8, 2026

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

Practical and applied

31 lessons with real examples

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

Curriculum

10 modules, 31 lessons — structured to learn step by step.

10 modules
31 lessons
~25h of content
Interactive quizzes
Free preview available What Computer Vision Is and the 2026 Landscape
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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
48 min
3 The Modern CV Stack: OpenCV, PyTorch, and the Ecosystem
48 min
1 Loading, Displaying, and Geometric Transformations
49 min
2 Filtering, Edges, and Classical Feature Detection
49 min
3 Data Augmentation and Preprocessing Pipelines
49 min
4 Building, Annotating, and Auditing Vision Datasets
49 min
1 Convolutional Neural Networks Explained
50 min
2 Building and Training a CNN Classifier in PyTorch
50 min
3 Transfer Learning and Fine-Tuning Pretrained Backbones
50 min
1 Object Detection Fundamentals: Boxes, IoU, NMS
50 min
2 YOLO in Practice
50 min
3 DETR and Transformer-Based Detection
50 min
4 Pose Estimation and Keypoint Detection
49 min
1 Semantic, Instance, and Panoptic Segmentation
50 min
2 Segment Anything: Promptable Segmentation with SAM 3.1
50 min
3 Training and Evaluating Segmentation Models
49 min
1 Vision Transformers (ViT) Explained
50 min
2 Vision-Language Models: CLIP to Modern VLMs
50 min
3 Using Vision-Language Models in Practice
50 min
4 Self-Supervised Learning and Vision Foundation Models
49 min
1 OCR and Document Understanding
50 min
2 Face Detection and Recognition: Technology and Legal Duties
50 min
3 Legal and Ethical Computer Vision
49 min
1 Video Analysis and Object Tracking
50 min
2 3D Vision and Depth Estimation
49 min
3 Generative Vision: Diffusion Models Basics
49 min
1 Evaluating Computer Vision Systems
49 min
2 Deployment and Edge Optimization
50 min
3 Monitoring, Drift, and Production MLOps for Vision
49 min
1 Final Assessment - Computer Vision with AI
45 min
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