Python (along with R) has become the dominant language in machine learning and data science. It is now commonly used to fit complex models to messy datasets. This two-day intensive course will equip you with the knowledge and tools to undertake a variety of tasks in a standard machine learning analytics pipeline. We stress the importance of data preparation, both in terms of data standardisation and feature selection, before tackling model building. The course covers regression and classification models, including, tree-based methods, clustering and sparse regression models. Model selection is introduced using cross-validation and bootstrapping.
It is expected that participants are comfortable using the Python programming language and common data structures. Some exposure to common statistical terms would be an advantage, but not essential Attendance of the Introduction to Python course or equivalent experience should be sufficient.
Introducing Machine Learning (ML)
An introduction to machine learning and the associated packages in Python, such as Numpy, Scipy, andSciKit-Learn.
Learn the why and how about preprocessing your data with scaling transformations and one hot encoding. We cover typical standardisation and normalisation procedures.
Introduction to Modelling
Introductory modelling techniques such as linear regression and how we move from a statistical model to a machine learning model.
Quantify the effectiveness of your models using training, validation and test sets plus techniques such as cross-validation. We discuss the different metrics that can be used to judge a model and which are appropriate
Techniques to avoid overfitting and to perform feature selection, such as lasso, ridge and elastic net regression.
An unsupervised learning technique for uncovering patterns and structure within data.
Some more advanced model fitting using algorithms such as gradient boosted trees and support vector machines.