From R to Machine Learning; an introductory course
purlPURL: https://gxy.io/GTN:P00006Comment: What is a Learning Pathway?
We recommend you follow the tutorials in the order presented on this page. They have been selected to fit together and build up your knowledge step by step. If a lesson has both slides and a tutorial, we recommend you start with the slides, then proceed with the tutorial.
This learning path aims to teach you the basics of Machine Learning using R. Initially you will learn how to code in R within the Galaxy platform, gaining some familiarity into how to wrangle and analyze data. Then, you will be guided through the various types of Machine Learning techniques, developing some simple models using R.
New to R and/or Machine Learning? Follow this learning path to get familiar with the basics!
Module 1: R in Galaxy
Get a first understanding of how to code using R fully using the Galaxy infrastructure. The first part will introduce the basic concepts of R, whereas the second part will focus on providing some advanced concepts around data manipulation.
Time estimation: 5 hours
Learning Objectives
- Know advantages of analyzing data using R within Galaxy.
- Compose an R script file containing comments, commands, objects, and functions.
- Be able to work with objects (i.e. applying mathematical and logical operators, subsetting, retrieving values, etc).
- Be able to load and explore the shape and contents of a tabular dataset using base R functions.
- Understand factors and how they can be used to store and work with categorical data.
- Apply common `dplyr` functions to manipulate data in R.
- Employ the ‘pipe’ operator to link together a sequence of functions.
| Lesson | Slides | Hands-on | Recordings |
|---|---|---|---|
| R basics in Galaxy | |||
| Advanced R in Galaxy |
Module 2: Machine Learning using R
Having some foundational understanding of how to code in R, this module will provide initially an overview of the different types of Machine Learning, and then will provide some practical, hands-on examples of creating ML models.
Time estimation: 3 hours
Learning Objectives
- Understand the ML taxonomy and the commonly used machine learning algorithms for analysing -omics data
- Understand differences between ML algorithms categories and to which kind of problem they can be applied
- Understand different applications of ML in different -omics studies
- Use some basic, widely used R packages for ML
- Interpret and visualize the results obtained from ML analyses on omics datasets
- Apply the ML techniques to analyse their own datasets
| Lesson | Slides | Hands-on | Recordings |
|---|---|---|---|
| Introduction to Machine Learning using R |
Editorial Board
This material is reviewed by our Editorial Board:
Fotis E. Psomopoulos
Funding
These individuals or organisations provided funding support for the development of this resource
