It’s a 5-day online course on March 31 - April 4 designed to help developers dive deep into the basics of the technologies and techniques behind Generative AI (Gen AI). Created by a team of Google’s ML researchers and engineers, this program includes both conceptual in-depth analyses and hands-on coding examples so you can tackle new Gen AI projects with confidence.
How does the intensive work?
📚 Daily Assignments: This includes the latest white papers, a companion podcast (generated by NotebookLM), and companion codelabs in AI Studio.
🎥 Daily Livestream Seminars and AMAs: Live everyday on Kaggle's YouTube channel, where the authors and course contributors will dive deeper into the topics and answer your burning questions. Plus, we've got fun surprises in store to keep the learning engaging.
🆕 Capstone project: Level up your skills and build your portfolio with a real-world capstone project. Complete at least one code lab during the 5 day course and the capstone project at the end to compete for prizes like Kaggle certificates, badges, swags and the recognition of being celebrated on Kaggle and Google’s social media channel. If you’re planning to come to Google Cloud Next, you get to connect with the Google experts in person at the kiosks and lightning talks. Learn more about the capstone project during the course.
👋 Hi there! I'm Rahul, and this repository documents my hands-on experience with Google’s 5-Day Generative AI Intensive Course (March 31 - April 4, 2025).
Whether you're a beginner exploring AI or an experienced developer, this repo serves as a structured guide with:
✔ Simplified whitepaper summaries (for non-tech learners!)
✔ Code lab implementations (with explanations)
✔ Key takeaways & real-world applications
✔ LinkedIn posts & community discussions
🔗 Follow my daily updates on LinkedIn
google-genai-intensive-2025/
├── daily-notes/ # Day-wise breakdowns
├── whitepaper-summaries/ # Simplified explanations
├── codelabs/ # Hands-on experiments
├── capstone-project/ # Final challenge submission
└── resources/ # Podcasts, livestream notes & more
🔹 Key Learnings:
- Evolution of LLMs (Transformers → Gemini)
- How to craft effective prompts (few-shot, chain-of-thought)
- Practical use cases (automated customer support, content generation)
🔹 Key Learnings:
- What are embeddings? (Turning text into numbers)
- Building a RAG (Retrieval-Augmented Generation) system
- Real-world use: Semantic search, recommendation engines
🔹 Key Learnings:
- How AI agents work (planning, memory, tools)
- Built a café ordering chatbot using LangGraph
- Case study: AI automating workflows (2.5x productivity boost!)
🔹 Key Learnings:
- Specialized models like Med-PaLM (medical AI) & SecLM (security AI)
- Fine-tuning Gemini for custom tasks
- Grounding LLMs with real-time data (Google Search API)
🔹 Key Learnings:
- Deploying AI models in production
- Monitoring & scaling LLM applications
- Google’s Vertex AI tools for Gen AI
Project Title: [Your Project Name]
Goal: [Brief description – e.g., "An AI-powered financial advisor using RAG"]
✅ Beginner-Friendly: Simplified explanations for non-technical learners
✅ Hands-On: Code labs with step-by-step breakdowns
✅ Community-Driven: Linked Discord discussions & LinkedIn reflections
✅ Real-World Ready: Practical applications beyond theory
- For Learners: Follow the daily notes to grasp concepts.
- For Developers: Check out the enhanced codelabs.
- For AI Enthusiasts: Join discussions on LinkedIn!
🌟 Star this repo if you found it helpful! (Top-right ⭐)
🎯 Final Thought:
"AI won’t replace humans, but humans using AI will replace those who don’t."
Keep learning, keep building! 🚀
🔹 Credits: Google’s Gen AI Team, Kaggle, & the amazing AI community.
🔔 Want updates? Watch this repo (top-right) to stay tuned!
If you’re also participating in this course, feel free to join the learning journey by opening issues or contributing via pull requests.
Feel free to reach out if you have any questions!