This is a tutorial on Machine Learning with R for the Melbourne Data Science Week, Melbourne, 29 May - 2 June 2017.
Note: Certificates have been sent out to all attendees of
the tutorial. If you have attended but haven't received a certificate,
please let me know. Thanks.
Course Title
Machine Learning with R: Association Rules, Text Mining and Social Network AnalysisPrerequisite
- Familiarity with scripting and/or programming
- Basic knowledge of data mining, especially of,
- Association rules
https://en.wikipedia.org/wiki/Association_rule_learning - Text mining
https://en.wikipedia.org/wiki/Text_mining - Social network analysis (SNA)
https://en.wikipedia.org/wiki/Social_network_analysis
- Association rules
Requirement
You will need to bring your own laptop. Please install the required software and R packages and download the datasets, slides and scripts below before coming to the course.
- Software and Packages
- R
http://www.r-project.org/ - RStudio (desktop edition)
http://www.rstudio.com/products/rstudio/download/ - R packages (please run the R script to install required R packages)
http://www.rdatamining.com/books/rdm/code/Install-R-packages.R - RStudio project archive [MLwR.zip], which contains all datasets, slides and scripts below. Alternatively, you may download individual files separately at links below.
- Datasets
- Titanic dataset
http://www.rdatamining.com/data/titanic.raw.rdata - Twitter dataset
http://www.rdatamining.com/data/RDataMining-Tweets-20160212.rds - Graph dataset http://www.rdatamining.com/data/graph.rdata
- Titanic dataset
- Slides
- R scripts [ZIP]
Course Outline
This is a course on machine learning with R. It will cover four sessions below. Each session will be of 45 minutes, composed of a 35-minute tutorial and a 10-minute lab.
- R Programming:
basics of R language and programming, parallel computing, and data import and export - Association Rule Mining with R:
mining and selecting interesting association rules, redundancy removal, and rule visualisation - Text Mining with R:
text mining, word cloud, topic modelling, and sentiment analysis, - Social Network Analysis with R:
graph construction, graph query, centrality measures, and graph visualisation
If you have any questions or feedback, please do not hesitate to contact me on yanchang <at> RDataMining.com. Thanks.