Computer Vision Basics
Understand how AI sees and interprets visual information. This path covers the fundamentals of computer vision from image classification to object detection, segmentation, and practical applications using both pre-built APIs and open-source models.
What You'll Learn
- Understand how AI processes and interprets images
- Learn image classification, object detection, and segmentation
- Use pre-built vision APIs from major cloud providers
- Run open-source vision models locally
- Build practical computer vision applications
- Understand multimodal models that combine vision and language
Course Lessons
How Computers See: Vision AI Fundamentals
18 min readUnderstand how convolutional neural networks and vision transformers process images — from pixels to features to understanding.
Image Classification in Practice
18 min readBuild image classification systems using pre-trained models and learn when to fine-tune versus train from scratch.
Object Detection and Tracking
20 min readImplement object detection using YOLO, DETR, and cloud APIs. Understand real-time detection for video and streaming applications.
Image Segmentation and Scene Understanding
18 min readLearn semantic and instance segmentation, and how modern models like SAM enable zero-shot segmentation of any object.
Multimodal Vision-Language Models
18 min readExplore models like GPT-5V, Gemini, and Claude that combine vision and language understanding for powerful visual question answering.
Read lesson →Building a Computer Vision Application
20 min readHands-on guide to building a practical computer vision application — from model selection through deployment and monitoring.
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