Professional Certificate in AI Engineering

Course overview

A rigorous, project-based programme designed to take learners with an existing technical foundation into professional AI Engineering. The programme covers advanced machine learning, deep learning fundamentals, model evaluation and optimization, AI application architecture, deployment of models as APIs, and applied generative AI system design, including retrieval-augmented generation and AI agents. Learners complete a substantial capstone project that mirrors real-world AI engineering work — from problem definition to deployed application.

Who it's for: Software developers, data analysts, data scientists, and technically-inclined professionals who already have basic Python experience and want to specialize in building and deploying AI systems professionally.

What you will learn

• Design and train machine learning models for real-world problems
• Understand deep learning fundamentals and neural network architectures
• Evaluate, tune, and optimize models for production use
• Build APIs to serve machine learning and AI models
• Design applied generative AI systems, including retrieval-augmented generation
• Understand the fundamentals of AI agents and tool-using systems
• Apply MLOps practices for model versioning, monitoring, and deployment
• Architect end-to-end AI applications from data to deployed product
• Evaluate AI system performance, cost, and reliability trade-offs
• Complete and present a full AI engineering capstone project

Curriculum

Tools you'll use

Projects you'll build

• Production-Ready Predictive Model with Deployed API
• Retrieval-Augmented Generation (RAG) Application
• AI Agent for a Real Business Workflow
• End-to-End AI Engineering Capstone Project

Prerequisites

• • Basic Python programming experience
• • Familiarity with fundamental machine learning concepts is helpful but not required
• • Comfortable using the command line at a basic level

Frequently asked questions