Move beyond watching tutorials. Throughout the course, you'll build hands-on Generative AI applications, understand how they work, and turn your best work into a portfolio you can discuss in interviews.
Build an AI application that answers questions from uploaded documents using Retrieval-Augmented Generation.
You'll practice: document ingestion, retrieval, embeddings, vector databases, and context-aware responses.
You'll be able to explain how an AI application retrieves relevant information before generating an answer.
Build an AI agent that uses the Model Context Protocol to pull live news sources and deliver structured, summarized updates on demand.
You'll practice: MCP fundamentals, tool connections, structured outputs, and multi-step agentic workflows.
You'll be able to explain how you connected an AI agent to live tools and turned unstructured information into a useful, structured briefing.
Build an AI agent that can follow instructions, use tools, and complete a sequence of tasks.
You'll practice: agentic workflows, tool use, human-in-the-loop steps, and workflow automation.
You'll be able to explain how an agent decides what action to take and where human approval belongs in the workflow.
Create an application that works with more than one type of input, such as text, audio, images, or video.
You'll practice: speech-to-text, text-to-speech, text-to-image generation, and multimodal workflow design.
You'll be able to explain how different AI capabilities can work together to solve a user problem.
Customize a language model on a focused dataset and compare its behavior against prompting and retrieval-based approaches.
You'll practice: model customization, fine-tuning workflows, and evaluating trade-offs between fine-tuning, prompting, and retrieval.
You'll be able to explain when fine-tuning is worth the cost compared to prompting or retrieval-based approaches.
Choose a real-world problem and build an end-to-end AI application around it.
You'll complete: problem definition, solution design, working prototype, project review, and portfolio presentation.
You'll have a substantial project you can discuss on your resume, LinkedIn, portfolio, and in interviews.
Every project is designed to help you understand not only how to build an AI application, but also how to explain the problem, approach, tools, and limitations behind it.
Follow a structured eight-week path, build practical AI applications, and complete a portfolio-ready capstone project.
Prefer to see the teaching first? Watch the Free Demo Class
