# Introduction to Machine Learning

*05 May 2020*

# Preface

*This course is based on original material by JJ Valetta from Easter 2019*

An introductory workshop on the field of machine learning. The focus will be on how to use these methods in practice using R and Python, rather than on the rigorous underlying mathematics. The target audience is anyone who wants to know what machine learning is, what problems it can solve and how we can solve them in practice using R or Python.

## Prerequisites

- Programming basics in either R or Python

## Learning outcomes

- Understand the key concepts and terminology used in the field of machine learning
- Build predictive models for clustering, regression and classification problems
- Apply machine learning algorithms in R/Python to a variety of real-world datasets
- Recognise practical issues in data-driven modelling

## Recommended reading

I highly recommend the following books:

## Software packages

- R:
`caret`

- Python:
`scikit-learn`

**Please make sure to read the documentation of any machine learning algorithm before using it!**

## Data files

All data files can be downloaded as a ZIP file from here.