Introductory Applied Machine Learning

Cartoon image of laptop screen of a blue brain

Eighteen playlists of tutorial videos created for the online distance learning postgraduate course – Introductory Applied Machine Learning.

The course is about the principled application of machine learning techniques to extracting information from data. The main area discussed is supervised learning, which is concerned with learning to predict an output, given inputs. A second area of study is unsupervised learning, where we wish to discover the structure in a set of patterns, i.e. there is no output “teacher signal”. The primary aim is to provide the student with a set of practical tools that can be applied to solve real – world problems in machine learning, coupled with an appropriate, principled approach to formulating a solution.


View the Introductory Applied Machine Learning playlists directly on Media Hopper Create



Linear Regression V1


Maths and Probability


Thinking about data


Naive Bayes


Decision Trees


Generalisation and Evaluation


Linear Regression


Logistic Regression






SVM Part 1


SVM Part 2


Nearest Neighbours




Gaussian Mixture Models


Principal Components Analysis


Hierarchical Clustering


Neural Networks




Header Image by mohamed Hassan from Pixabay