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This repository contains the material related to my book Intuitive Machine Learning, available here. The table of contents is available here. To access the main folder, click here.

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.