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# introduction
Most free courses provide surface-level theory and certificates, which are often forgotten within a week. Fortunately, google and Kaguru We have worked together to provide a more substantial alternative. The 5-day intensive Generative AI (GenAI) course covers fundamental models, embedding, AI agents, domain-specific large-scale language models (LLM), and machine learning operations (MLOps) through a week of whitepapers, hands-on code labs, and live expert sessions.
The second edition of the program attracted over 280,000 registrants, setting a Guinness World Record for largest virtual AI conference in one week. All course materials are now self-paced Kaggle study guidecompletely free. This article describes the curriculum and why it is a valuable resource for data professionals.
# Check course structure
Focus on a specific GenAI topic each day using a multichannel learning format. The curriculum includes: Whitepaper written by Google machine learning researchers and engineers, and a summary podcast created by AI. notebook LM.
Hands-on code labs run directly on Kaggle notebooks, so students can apply concepts right away. The original live version featured a YouTube livestream with expert Q&A sessions and a Discord community of over 160,000 learners. Get conceptual depth from whitepapers and immediately apply those concepts to code labs. Gemini API, Langgrafand Vertex AIthe course maintains a steady momentum between theory and practice.
// Day 1: Exploring basic models and rapid engineering
The course begins with the essential building blocks. you will Explore the evolution of the LLM — From the original Transformer architecture to the latest fine-tuning and inference acceleration techniques. The Prompted Engineering section goes beyond basic teaching tips to provide practical ways to effectively guide model behavior.
The associated code lab works directly with the Gemini API to test various prompting techniques in Python. If you’ve used LLM before, but haven’t delved into how temperature settings or few-shot prompt structures work, this section will quickly address those knowledge gaps.
// Day 2: Implementing embedding and vector databases
Day two focuses on embedding, moving from abstract concepts to practical applications. you, Geometric methods used to classify and compare text data. Next, this course introduces vector stores and databases, the infrastructure required for large-scale semantic search and search augmented generation (RAG).
The practical part involves building a RAG question answering system. This session will demonstrate how organizations can integrate LLM output with real data to reduce illusions, and provide a functional explanation of how embedding is integrated into operational pipelines.
// Day 3: Developing a generative artificial intelligence agent
Day 3 covers systems that extend beyond simple prompt-response cycles by connecting AI agents, or LLMs, to external tools, databases, and real-world workflows. you will Learn the core components of the agentiterative development processes, and practical applications of function calls.
The code lab includes interacting with the database through function calls and building an agent ordering system using LangGraph. As agent workflows become the standard for production AI, this section provides the technical foundation needed to connect these systems together.
// Day 4: Analyzing large-scale domain-specific language models
This section focuses on specialized models adapted to specific industries. Explore examples such as Google’s SecLM for cybersecurity and Med-PaLM for healthcare, including details. Use of patient data and safety measures. Although general-purpose models are powerful, they often require fine-tuning for specific domains if high accuracy and specificity are required.
Hands-on exercises include grounding a model using Google search data and fine-tuning a Gemini model for custom tasks. This lab is particularly useful because it shows you how to adapt a basic model using labeled data. This skill will become increasingly important as organizations move towards bespoke AI solutions.
// Day 5: Master machine learning operations for generative artificial intelligence
The final day covers deployment and maintenance of GenAI in a production environment. you learn How traditional MLOps practices apply to GenAI workloads. This course also demonstrates Vertex AI tools for managing underlying models and applications at scale.
Although there is no interactive codelab on the final day, the course provides a thorough code tutorial and a live demo of Google Cloud’s GenAI resources. This provides important context for anyone planning to move a model from a development notebook to a production environment with real users.
# ideal audience
For data scientists, machine learning engineers, and developers I want to specialize in GenAIthis course offers a rare balance of difficulty and accessibility. The multi-format approach allows learners to adjust the depth based on their level of experience. Even beginners with a solid foundation in Python can complete the curriculum.
The self-paced Kaggle study guide format allows for flexible scheduling, whether you want to complete it over a week or a weekend. Notebooks run on Kaggle, so no local setup is required. All you need to get started is a phone-verified Kaggle account.
# final thoughts
Google and Kaggle have created high-quality educational resources that are available for free. This course provides a comprehensive overview of the current GenAI landscape by combining expert-authored whitepapers with immediate practical applications.
Our high enrollment and industry recognition reflect the quality of our educational materials. Whether your goal is to build a RAG pipeline or understand the underlying mechanics of AI agents, this course will provide you with the conceptual framework and code you need to succeed.
nara davis I’m a software developer and technical writer. Before focusing on technical writing full-time, she was able to work as a lead programmer at a 5,000-person experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.
