
University of St Andrews - Online
Course in Advanced: End-to-End Machine LearningOnline United Kingdom
DURATION
41 Days
LANGUAGES
English
PACE
Part time
APPLICATION DEADLINE
Request application deadline
EARLIEST START DATE
Jul 2025
TUITION FEES
GBP 1,800
STUDY FORMAT
Distance Learning
Introduction
Master advanced machine learning techniques, including deep learning and neural networks, for sophisticated data analysis.
This short course will give you the tools to understand the concepts and technologies that underpin modern deep learning using artificial neural networks (ANNs).
The course introduces you to basic neural networks using the scikit-learn Python package. It covers the key concepts, techniques and technologies for training and prediction using multilayer perceptrons and the Keras Python package.
The course also includes specialised and advanced coverage of modern deep learning techniques and tools, based on both the Keras and TensorFlow Python packages.
You will learn:
- custom neural net models using Tensorflow
- deep computer vision using convolutional neural networks
- modelling time-series data with recurrent neural networks and
- artificial intelligence (AI) generation of images using autoencoders, generative adaptive networks, and diffusion techniques.
Advanced Python code is supplied and explained for each topic.
Your primary learning outcome is the ability to deploy and assess the state-of-the-art technologies that underpin modern AI-based machine learning and data science.
Gallery
Ideal Students
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 deep learning using artificial neural networks (ANNs).
The topics explain the key methods used to derive predictive models using multilayer perceptrons, convolutional and recurrent neural networks (CNNs and RNNs), and generative AI to produce high-quality new data.
The ability to perform Deep Learning workflows is a core skill in many fields, including:
- finance (prediction of future stock values)
- healthcare (tumour detection in scans)
- marketing (personalising the user experience).
Admissions
Faculty
Program delivery
Teaching format
This 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.
The time commitment is typically six to eight hours per week.