Team
AI For Global Goals
Since 2019, AI for Global Goals has trained 8,000+ practitioners through OxML, Elandi, and the Enterprise AI Academy — a single mission, three programmes, one continuously expanding alumni network spanning research, industry, and policy.
Learning Plans
Statistical Machine Learning
Master the mathematical foundations and statistical methods underlying modern machine learning. This certification covers probability theory, Bayesian modeling, optimization, and advanced statistical inference techniques essential for rigorous ML research and development.
Representation Learning
Explore cutting-edge techniques for learning powerful data representations. This certification covers self-supervised learning, transformers, multimodal models, and generative AI, preparing you for advanced roles in modern deep learning and foundation model development.
Evals for AI Products — from model.fit() to market.fit()
This learning plan covers the full landscape of evaluation for AI systems, moving from classical machine-learning diagnostics to the challenges of modern generative, agentic, multimodal, and enterprise-scale deployments. Learners progress through foundational metrics, LLM and agent evaluation, multimodal testing, domain-specific benchmarks, operational and safety assessments, and commercial ROI evaluation. By the end, participants can design rigorous eval suites that reflect scientific best practices, real-world constraints, and business outcomes. The course blends technical depth with practical workflows that are essential for trustworthy, high-impact AI products.