Data Science & Machine Learning Training

Machine learning creates value only when it escapes the notebook: when teams understand both the models and the data platforms that feed them. Our data science and ML courses are built for engineering teams who need working skills, not just theory.

Machine learning with Python

Python Machine Learning takes developers and analysts hands-on through the ML workflow: preparing data, choosing and training models, evaluating results honestly and avoiding the classic traps of leakage and overfitting. The course uses the standard Python data stack that real teams use in production, and, like all our training, is taught live by a practitioner who can connect every concept to your actual use cases.

The data platform underneath

Models are only as good as the data flowing into them. That's why this curriculum includes Apache Kafka for Java/Scala developers (building producers, consumers and stream-processing applications on the de-facto standard event streaming platform) and Kafka administration for the teams operating those clusters: partitioning, replication, monitoring and scaling messaging infrastructure reliably.

Many organisations combine these courses into a track: engineers learn to build streaming data pipelines while analysts and developers level up on the ML workflow that consumes them.

Taught for engineers, adapted to your data

Private sessions can be calibrated to your team's background (stronger on the statistics for developer-heavy groups, stronger on the engineering for analyst-heavy groups), and examples can be aligned with your domain. Sessions are small-group and lab-driven throughout.

Flexible delivery

All courses in this curriculum run as private sessions (online or at your premises across EMEA and beyond) with scheduling that fits around delivery work. Selected courses also appear as open-enrollment events.

Browse the data science and machine learning courses below, or request a free quote for your team.