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.

Learning Outcomes:

• Understand where machine learning can be used, and what constitutes a good machine learning problem.
• Explore how to identify and collect the right data and make it suitable for machine learning.
• Understand what "Big Data" is and appreciate some of the challenges it presents?
• Learn about the different types of machine learning model and understand which is most suitable for different types of data?
• Learn some straightforward machine learning models that can be applied to your own data.
• Understand how to utilise Python and scikit-learn to program solutions to a data problem.

Pre-requisites:

• We recommend familiarity with programming in Python. We will provide Python programming self-learning material in advance for the course attendees.
• An understanding of basic statistical concepts (correlation, significance etc.) would also be useful.