The course aims to teach students Python programming from scratch and, in parallel, to introduce them to the basics of 'traditional' machine learning (ML) in biology. 'Traditional' ML means that the study of artificial neural networks is not included in this course. 

In terms of skills, students will be able, by the end of the course, to implement ML steps using the Python language and libraries such as Numpy, Pandas, Matplotlib and Scikit-Learn. In particular, they will be able to import the data, clean the data, split the data (creating training and test sets), implement a machine learning model and evaluate its performance. 

The ambition of this course is to teach students to program in Python and apply what they have learnt to develop a machine learning pipeline, and not to become machine learning experts. However, students who become passionate about the discipline will be given the tools to independently develop the ability to work in the field of ML applied to biological data. 

The course is fully practical. It is strongly recommended to attend class in order to acquire the skills required to successfully achieve learning outcomes and pass the exam.