Welcome to the Machine Gnostics Tutorials¶
These tutorials are designed to help you master the Machine Gnostics framework for robust, interpretable, and assumption-free data analysis and machine learning. Whether you are a beginner or an advanced user, you will find step-by-step guides, practical code examples, and conceptual explanations to accelerate your learning.
Getting Started¶
Before you begin, please ensure that Machine Gnostics is installed correctly in your environment.
- See the Installation Guide for setup instructions.
Note
Machine Gnostics is based on Mathematical Gnostics theorems. The procedures and inference of results may differ from standard statistical methods, offering new perspectives and robust diagnostics.
What You'll Learn¶
Data Analysis¶
- Machine Gnostics Measures: Understand the core measures and how they differ from traditional statistics.
- Gnostics Distribution Functions: Learn to fit and interpret GDFs for your data.
- Gnostics Tests Perform gnostic test on given data samples.
- Cluster Analysis: Discover how to identify clusters and estimate their bounds (basic and advanced workflows).
- Interval Analysis: Estimate robust intervals (tolerance, typical, and cluster) for scientific and engineering data (basic and advanced workflows).
- Uncertainty Analysis: Apply Gnostic methods to real-world uncertainty quantification.
Supervised Machine Learning¶
- Linear Regression: Fit and interpret robust linear models.
- Polynomial Regression: Model nonlinear relationships with resilience to outliers.
- Logistic Regression: Perform robust binary classification with flexible probability outputs.
- MLflow Integration: Track experiments and manage model deployment.
Example Notebooks¶
Explore hands-on Jupyter notebooks for each topic in the Examples section.
Ready to begin?
Start with the first tutorial or jump to the topic that interests you most. Each tutorial is self-contained and includes code, explanations, and practical tips.