Practical Guide to Machine Learning: Defining Problem Scope and Assessing Model Requirements

Self-Learning Course

Take this course at your own pace through pre-recorded video and online resources.

Aimed at intermediate learners, this 4 half days’ course will walk you through building a machine learning model from start to finish. From collecting good quality, unbiased data to preparing the data for modelling and exploring some simple machine learning models.

We’ll also share practical steps to help you work with your data and illustrate example applications by sharing some of our diverse case studies spanning from healthcare, production lines and even how machine learning has helped combat counterfeiting in the Scotch Whisky sector.

With opportunities to consider how to apply machine learning to your own data problems, this course is suitable for anyone who wants to learn how to handle and analyse their data using freely available open source tools. It will be particularly useful for those in operations, engineering, IT or production managers and directors.

Pre-requisites:

• We recommend familiarity with programming in Python
• An understanding of basic statistical concepts (correlation, significance etc.) would also be useful.

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