Vision-language models represent a breakthrough in AI, enabling machines to understand images and generate meaningful descriptions, answer questions, and extract insights. This tutorial guides you through practical implementations using industry-leading tools like GPT-4 Vision and Claude's vision capabilities, plus open-source alternatives such as LLaVA for production environments. You'll build real-world projects that demonstrate how to analyse images, extract text via OCR, generate detailed captions, answer visual questions, and create intelligent systems that combine sight and language. Whether you're developing content platforms, accessibility tools, or data analysis systems, you'll learn the techniques that power modern multimodal AI applications. By the end of this course, you'll confidently integrate vision-language models into your projects and understand when to use commercial APIs versus open-source alternatives for optimal results and cost efficiency.

Lessons

  1. Introduction to Vision-Language Models — Understanding multimodal AI, architecture basics, and real-world applications (+100 XP)
  2. Getting Started with GPT-4 Vision — API setup, making your first image analysis request, and handling responses (+125 XP)
  3. Claude's Multimodal Capabilities — Comparing APIs, leveraging Claude for nuanced analysis, and cost considerations (+125 XP)
  4. Open-Source Models: LLaVA and Alternatives — Local deployment, fine-tuning options, and production-ready setup (+150 XP)
  5. Building Image Analysis Projects — Image captioning, visual QA systems, and creating intelligent workflows (+150 XP)
  6. Advanced Techniques: OCR and Data Extraction — Extracting text, structured data, and handling complex documents (+150 XP)
  7. Deployment and Optimisation — Production considerations, error handling, cost optimisation, and scaling strategies (+150 XP)