This section contains a number of tutorials on basics of Python programming, machine learning (ML), and feature selection & ranking. Required Python version is 3.6 or above.
The fundamental philosophy behind these tutorials is that, rather than being variants of user manuals, each tutorial attempts to address one particular learning problem in a coherent and systematic fashion. A further philosophy is that these tutorials attempt to "include everything you need and nothing you don't".
These tutorials are based on parts of the course materials of MATH2319 - Machine Learning taught by Dr. Vural Aksakalli at RMIT University. Assistance provided: Zeren Yenice (feature selection & ranking), Yong Kai Wong (ML tutorials), and Imran Ture (Python programming). Please do not post portions of these tutorials elsewhere without proper citation (unless a tutorial is referenced at the end).
The reference book for the ML-specific tutorials is the FMLPDA text below:
Kelleher, John D., Brian Mac Namee, and Aoife D’Arcy. 2015. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies. MIT Press (website)