Overview

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Become an AI Application Developer capable of engineering sophisticated systems that can think, search, and act autonomously to solve complex real-world challenges. This specialization provides a comprehensive look into the Google Gemini ecosystem. You will use the Gemini API’s most advanced features, including model thinking parameters for transparent reasoning, grounding with Google Search, and structured output with JSON Schema. Throughout the courses, you will progress from basic text generation to building functional AI agents and autonomous processors. You will utilize Google AI Studio for rapid prototyping and experiment with model parameters before deploying production-ready applications directly to Cloud Run. You’ll also learn to orchestrate complex tasks using function calling to connect Gemini to external tools and data. Upon completion, you’ll be able to: Build and deploy multi-capability AI agents using the Gemini API, function calling, and built-in tools. Implement cost-effective applications by strategically mastering model selection between Gemini Pro and Flash. Architect reliable systems that use structured JSON output and "thinking" to ensure high-quality, parseable results. Prerequisites: The specialization is designed for developers and engineers who have a background in object-oriented programming (Python or JavaScript) and fundamental API usage/REST concepts. You'll also need to be comfortable writing and debugging code.

Syllabus

Course 1: Getting Started with Google Gemini API
Course 2: Exploring AI with Google AI Studio
Course 3: Gemini Capstone Project

Courses

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This course gives you a good look at Google AI Studio. You will learn to transition from natural language concepts to production-ready code by experimenting with prompt engineering and real-time visualization of token usage. The curriculum bridges the gap between manual experimentation and automated development, teaching you to refine AI behavior before implementation. By using this interface, you will significantly accelerate your development lifecycle—prototyping complex ideas, testing model trade-offs, and deploying functional applications directly to the cloud. By the end of this course, you will be able to: - Manage API keys and explore advanced features like Maps integration and Google Search grounding. - Develop a systematic workflow to evaluate and refine prompts across Flash and Pro models. - Export UI experiments into clean, functional code for Python or JavaScript environments. - Transition from a prototype to a live application using seamless Cloud Run integration.
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This course provides an in-depth exploration of advanced AI agent development, focusing on function calling, tool integration, and orchestrating complex tasks. You will learn to define custom functions to extend the capabilities of large language models, architecting autonomous systems that combine built-in tools and structured output. The curriculum covers the entire lifecycle of a sophisticated AI application, from designing robust conversation flows with error handling to analyzing the economics of token usage. By the end of this course, you will be able to: - Build multi-capability systems that combine custom functions, built-in tools, and intelligent model selection. - Define and integrate custom function schemas to allow your AI to interact with external data and services. - Create reliable, multi-step agent behaviors with proper asynchronous handling and security best practices. - Monitor token usage and project costs to ensure sustainable and cost-effective application scaling. - Launch your final AI application rapidly using Google Cloud Run integration for professional-grade hosting.
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This course introduces Google Gemini API. You’ll move beyond basic chat interfaces to building intelligent, high-performance systems. You will use foundational API setup and progress to sophisticated features like function calling and structured output. You’ll use decision-making to balance cost and speed using Gemini Pro and Flash models. By leveraging Gemini’s unique "thinking" capabilities and web-grounding tools, you will learn to build reliable, transparent AI solutions that process data with precision at scale. By the end of this course, you will be able to: - Manage API keys and set up development environments in Python or JavaScript. - Choose between models based on cost, latency, and performance requirements. - Use "thinking" and thought summaries to debug prompts and improve transparency. - Integrate real-time data using built-in tools like Google Search and URL Context. - Use JSON Schema to produce consistent, parseable outputs for downstream logic.