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Overview
What you will study
This Intermediate End-to-End Machine Learning course offered by University of St Andrews is a self-paced online learning short course with lecture content, interactive elements, and access to a masterclass with the course leader after completion of the course. New content for the course will be released each.
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.
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Visit programme websiteProgramme Structure
The program focuses on:
- Classification explains how best to predict discrete classes (for example, accept or reject credit applications).
- 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.
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Visit programme websiteKey information
Duration
- Part-time
- 42 days
Start dates & application deadlines
- StartingApplication deadline not specified.
Language
English
Delivered
Online
- Self-paced
Disciplines
Machine Learning View 25 other Short Courses in Machine Learning in United KingdomExplore more key information
Visit programme websiteAcademic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
- The course is aimed at professionals with a high level of numeracy who are seeking to understand the core concepts, methods and technologies that underpin modern machine learning.
- This course is suitable for those who are competent in programming using Python (including notebooks, packages, data manipulation, design and use of pipelines, model evaluation functions) but are not expert programmers.
Technological requirements
- A good internet connection is more important than powerful computational equipment.
Make sure you meet all requirements
Visit programme websiteTuition Fee
To always see correct tuition fees
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International
1800 GBP/fullTuition FeeBased on the tuition of 1800 GBP for the full programme during 42 days. -
National
1800 GBP/fullTuition FeeBased on the tuition of 1800 GBP for the full programme during 42 days.
Funding
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