Build Full Stack AI Solutions using Capstone Project

Course Overview Predictive AI and Generative AI are increasingly used together, yet many teams struggle to move from isolated models, prompts, or chatbots to reliable, end-to-end AI solutions. This course addresses that gap by teaching how to design and build full-stack AI systems that combine predictive models, large language models (LLMs), structured knowledge, and agentic workflows. The program emphasizes hands-on learning and real-world use cases, reinforced through a guided capstone project. Objectives Participants will learn to frame business problems for AI, work with structured and unstructured data, build and evaluate predictive models, design effective prompts, apply Taxonomy-Augmented Generation (TAG) and Retrieval-Augmented Generation (RAG), and design agentic and multi-agent AI systems. Hands-on exercises and a capstone project integrate these concepts into practical AI solutions. Prerequisites Basic programming knowledge, general computer literacy, and an analytical mindset are expected. Prior exposure to data analysis, machine learning concepts, or tools such as ChatGPT or similar AI platforms is beneficial. Audience Software engineers, data scientists, ML engineers, architects, and technical professionals seeking practical skills in building, integrating, and deploying modern AI solutions. Key Learning * Build predictive and generative AI components and integrate them into end-to-end solutions * Use TAG and RAG to represent knowledge and ground LLM responses * Design agentic and multi-agent AI systems for coordinated, reliable workflows Outcomes: Learners gain the ability to design, test, and deploy advanced generative AI solutions, showcase mastery through a capstone project, and earn a Credly certification recognizing expertise in LLM accuracy and integration. This program bridges theory and practice, preparing participants to deploy reliable, domain-specific AI systems across industries such as healthcare, finance, and education.