Machine learning careers are built on a small number of durable foundations that outlast any single framework or model release. This track covers them in order: the maths and data handling you cannot skip, the classical algorithms that still solve most business problems, the neural-network concepts behind modern systems, how large language models work and are used, and how a model becomes a reliable, monitored, responsibly deployed service. Each lesson ends with an action step that adds to a project portfolio.

For developers, analysts and career changers who want a clear, honest map of the field rather than a tour of this year’s tools.