Course Details

Course Outline
Learning Outcomes
Materials
Prior Knowledge

Course Outline

Introduction to Machine Learning: An introduction to machine learning and the associated packages in Python, such as Numpy, Scipy, and SciKit-Learn.
Data Preprocessing: 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.
Model Assessment: 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.
Regularisation: Techniques to avoid overfitting and to perform feature selection, such as lasso, ridge and elastic net regression.
Clustering: An unsupervised learning technique for uncovering patterns and structure within data.
Advanced Techniques: Some more advanced model fitting using algorithms such as gradient boosted trees and support vector machines.

Learning Outcomes

By the end of the course, participants will understand…

how to build and quantitatively assess a variety of models suitable for a range of problems.
the importance of data preprocessing and regularisation
confident compare the efficacy of their models using a rigorous training and testing framework
how various types of models operate
some modern, state of the art machine learning techniques.

Materials

Prior Knowledge

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.

Attendee Feedback

“The course was well designed and structured, for us students who know little ML. It introduced me to new tools that I will definitely make use of in my research. Jack was also a great tutor, very knowledgeable and patient with our questions.”
“The balance between theory and the practicals to apply what we had just learned was great.”
“Jack was great in answering all our theoretical questions so that we could get a better grasp of the underlying concepts and intuition.”

Training Course Details

Machine Learning with Python

Course Level: Intermediate

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