Machine Learning

This page contains machine learning (aka data science/data mining/statistical inference) resources I have created like tutorials, notes etc. and useful links to other websites.

In the deep-learning-pipelines github repo there are notebooks with some end-to-end pipelines I've built as examples, while in the ml-rants github repo there are notebooks with various machine learning examples that do not include neural networks. I also keep a curated list of links in my ml-directory github repo.

Book

Along with Themis Diamantopoulos, Michail Papamichail and Andreas Symeonidis, I have co-authored a book entitled: Practical Machine Learning in R.

The book is about quickly entering the world of creating machine learning models in R. The theory is kept to minimum and there are examples for each of the major algorithms for classification, clustering, feature engineering and association rules.

Blog posts

Blog posts on Machine Learning:

• Preprocessing

  • On Collinearity and Feature Selection

Machine Learning Tutorials in R

The Machine Learning Tutorials in R mini-site, contains tutorials of classic machine learning algorithms and how they are used within the R statistical environment. The mini-site has tutorials about:

• Introduction to R
• Classification
• Clustering
• Data Engineering
• Other ML tasks

I used R markdown and R studio for creating the mini-site. The sources can be found here.

Datasets

My github repo containing links to datasets or site hosting datasets for machine learning tasks:

• My currated collection in GitHub including 3rd party 'awesome' collections.

Lists

• awesome-datasience - awesome list for data science

My Learning Deep Learning notes

Notes I've kept while studying Deep Learning. Under development.