Google has made available a set of ten free artificial intelligence courses aimed at learners with no prior experience. The modules span generative systems, language models and responsible AI frameworks, with short durations designed for quick completion.Each course is hosted on Google learning platforms and does not require sign-up fees or advanced technical background. Learners can access structured content covering theory, practical demonstrations and tool-based exercises.Google Free AI Course Catalogue OverviewGoogle’s free AI learning catalogue includes ten modular courses spanning introductory concepts and applied machine learning techniques. The programme is structured for independent study, with each module designed to be completed in under an hour or through guided practice sessions.Foundational generative AI and language model coursesIntroduction to Generative AI provides a short 45-minute module explaining how generative systems work and how applications can be built using Google tools. Introduction to Large Language Models explains the role of LLMs, their use cases and methods for improvement. Introduction to Responsible AI sets out principles used in ethical AI development, outlining fairness and safety considerations. Prompt Design in Vertex AI focuses on crafting instructions for text and image generation using Google’s AI platform with practical exercises.Model architecture and transformer based learning toolsIntroduction to Image Generation explains how systems create realistic visuals from data and outlines core techniques behind image synthesis. Encoder-Decoder Architecture describes how machines process and translate language while summarising text. Attention Mechanism introduces how models prioritise relevant information in sequences. Transformer Models and BERT Model present advances in contextual language understanding, with completion offering a digital badge.Applied AI projects and generative studio toolsCreate Image Captioning Models enables learners to build systems that describe images by combining visual recognition with language generation techniques. The course focuses on training models that connect image inputs with descriptive text outputs through structured datasets.The course also explains how image captioning models are evaluated using accuracy and relevance metrics across varied datasets.Introduction to Generative AI Studio introduces Google’s application building environment for generative systems, allowing users to test and deploy AI-driven ideas through guided demonstrations and interactive tools.Generative AI Studio also provides pre-built templates for prototyping applications, supporting integration with text and image models. It enables learners to experiment with deployment workflows, prompt testing and iterative refinement of outputs within a controlled environment designed for structured learning across multiple generative AI use cases.Google’s free AI learning lineup covers generative tools, LLMs and transformer models for practical skill building1. Encoder-Decoder Architecture: Understanding how machines translate languages and summarise text using structured model building blocks.2. Introduction to Responsible AI: Learning how Google applies fairness principles and ethical frameworks in AI development.3. Attention Mechanism: Exploring how AI systems focus on relevant parts of text and images to improve predictions.4. Introduction to Generative AI: A short beginner course explaining how generative systems create content and applications using Google tools.5. Create Image Captioning Models: Building systems that generate descriptive text from images using combined vision and language techniques.6. Introduction to Large Language Models: Learning how large language models work, where they are used and how they can be improved.7. Prompt Design in Vertex AI: Practising how to structure prompts to generate accurate text and images using Google’s AI tools.8. Introduction to Generative AI Studio: Exploring Google’s platform for building, testing and deploying generative AI applications.9. Transformer Models and BERT Model: Understanding transformer architecture and how BERT improves contextual language understanding.10. Introduction to Image Generation: Learning how AI systems generate realistic images using data-driven and physics-inspired methods.
