The following material is currently available in the relevant sub-folders, here. Eventually, the purpose is to write two new books containing the material in question as well as some future articles, and integrating the best from my previous book Statistics - New Foundations, Toolbox, and Machine Learning Recipes, available here. At this point, the first book Intuitive Machine Learning is almost finished (to be published in October 2022) and will be used as core material for my upcoming classes on the subject.

Python code:

• HDT.py: Hidden decision trees (ensemble method). Described in my article Advanced Machine Learning with Basic Excel, available here.
• brownian_path.py, brownian_var.py: Described in my article Weird Random Walks: Synthetizing, Testing, and Leveraging Quasi-randomness, available here.
• fuzzy.py: Described in my article Interpretable Machine Learning: Multipurpose, Model-free, Math-free Fuzzy Regression, available here.
• fittingCurve.py, fittingEllipse.py, mixture1D.py: Described in my article Machine Learning Cloud Regression: The Swiss Army Knife of Optimization, available here.

See also randomNumbersTesting.py, in this folder. It is part of my article Detecting Subtle Departures from Randomness available here.

Spreadsheets:

HDTdata4Excel.xlsx: Hidden decision trees (ensemble method). Described in my article Advanced Machine Learning with Basic Excel, available here.
• shapes4.xlsx: Described in my article Computer Vision: Shape Classification via Explainable AI, available here.
• regression5.xlsx, regression5_Static.xlsx: Described in my article Interpretable Machine Learning on Synthetic Data, and Little Known Secrets About Linear Regression, available here.
• linear2-small.xlsx: Described in my article Gentle Introduction to Linear Algebra, with Spectacular Applications, available here.
• fuzzyf2.xlsx: Described in my article Interpretable Machine Learning: Multipurpose, Model-free, Math-free Fuzzy Regression, available here.