I've spent 22+ years designing and building real technology systems inside large, regulated organizations — cloud architecture, enterprise data platforms, and machine learning in production. Not prototypes. Systems that had to work, scale, and stand up to an audit.

I started Real Numbers because most technical training sits at one of two extremes. Either it's academic — heavy on theory, light on anything you can actually run — or it's a shallow tour of a tool that will be obsolete in a year. I think there's a better path: start from plain-English intuition, build up honestly, and put working code in your hands at every step.

So that's how I teach. Every lesson is short and focused. Every concept is grounded in a concrete picture before any equation or abstraction appears. The hands-on labs mean you finish having built something yourself, not having watched someone else build it. And I always explain the trade-offs, because knowing why an architecture was chosen matters more than memorising how to configure it.

What I teach

Artificial intelligence and machine learning — supervised and unsupervised learning, deep learning, model evaluation, and the MLOps practices that move a model from a notebook into production. Generative AI and large language models — prompt design, retrieval-augmented generation, agentic systems, evaluation and guardrails. Data engineering and platform architecture — pipelines, warehouses and lakehouses, streaming, data modelling, governance, lineage and data quality. Cloud architecture across AWS, Azure and Google Cloud, including the cost, security and scalability decisions behind each design. Python for data and AI work. And emerging areas such as quantum computing and quantum machine learning with Qiskit, for engineers who want to understand where the field is genuinely heading.

Who these courses are for

Software and data engineers levelling up. Architects moving into an unfamiliar domain. Data scientists who want their models to survive contact with production. And technology leaders who need to understand what their teams are building well enough to make good decisions about it.

Every course stands on its own, so start wherever your gap is.