Building Gen AI App 12+ Hands-on Projects with Gemini Pro Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
This comprehensive course guides beginners through building practical Generative AI applications using Google's Gemini Pro and modern development tools. With a strong emphasis on hands-on learning, the curriculum spans eight modules covering core AI concepts, multimodal processing, data integration, and deployment. You'll complete over 12 real-world projects, gaining experience with LangChain, Streamlit, and Gemini APIs. The course requires approximately 6-8 hours of commitment and is structured to take learners from setup to full-stack application deployment.
Module 1: Introduction to Gemini Pro & Project Setup
Estimated time: 0.5 hours
- Overview of Gemini Pro capabilities and architecture
- Setting up the Python development environment
- Configuring API keys for Gemini
- Initial project structure and dependencies
Module 2: LangChain & Gemini API Fundamentals
Estimated time: 0.75 hours
- Integrating LangChain with Gemini Pro
- Orchestrating LLM workflows using LangChain
- Prompt engineering techniques
- Implementing chaining and memory handling
Module 3: Building Your First Gemini AI App
Estimated time: 1 hour
- Creating a chatbot using Streamlit
- Integrating Gemini API into Streamlit
- Handling user inputs and session states
- Generating dynamic AI responses
Module 4: Multimodal Applications
Estimated time: 1 hour
- Processing text and image inputs with Gemini
- Building image captioning systems
- Implementing image classification features
- Developing visual search applications
Module 5: Data-Driven Applications
Estimated time: 1.25 hours
- Integrating CSV and Google Sheets data sources
- Connecting to external APIs for dynamic data
- Automating AI-powered summaries and reports
- Generating visualizations using AI insights
Module 6: Code Generation & Productivity Tools
Estimated time: 1 hour
- Building AI code assistants
- Generating code templates and snippets
- Auto-documenting functions and scripts
- Creating productivity-enhancing AI tools
Module 7: Deployment & UI Optimization
Estimated time: 0.75 hours
- Deploying Streamlit apps with responsiveness
- Implementing error handling and UX improvements
- Hosting on GitHub and cloud platforms
Module 8: Capstone Projects & Best Practices
Estimated time: 1 hour
- Developing full-stack GenAI applications
- Applying performance tuning techniques
- Enhancing user experience and scalability
Prerequisites
- Intermediate knowledge of Python programming
- Familiarity with basic machine learning concepts
- Basic understanding of APIs and web frameworks
What You'll Be Able to Do After
- Build end-to-end Generative AI applications using Gemini Pro
- Design intelligent chatbots and multimodal AI tools
- Integrate AI with data sources like CSVs and Google Sheets
- Deploy scalable AI applications using Streamlit and cloud platforms
- Create code generation and automation tools with LangChain and Gemini