University of St Andrews - Online

Course in Intermediate: End-to-End Machine Learning

Enhance your machine learning expertise by delving into more complex algorithms and real-world applications.

This short course is aimed at professionals who are seeking to understand the concepts and technologies that underpin modern machine learning.

In this course, you will learn about modern machine learning methods through five topics:

• Classification explains how best to predict discrete classes, for example, accept or reject credit applications.
• Training Models introduces the methods used to solve the core optimisation problem: which variant of a class of models has the least error?
• Trees & Random Forests explores how tree models can be derived, extended and deployed to produce models with validated estimates of performance on new data instances.
• Dimensionality Reduction covers the rationale for and methods applicable to reducing the number of features used in predictive machine learning models.
• Unsupervised Learning considers how to learn and deploy models for which there is no target variable.

Advanced Python code is supplied and explained for each topic. Your key learning outcomes are to determine what models are applicable for different data and objectives, and to conduct hyperparameter-tuning or model-selection as appropriate to the model.