math4ml 0.1.1

A lightweight math and statistics library for machine learning with educational tutorials and interactive examples.

📘 math4ml — A Lightweight Math & Stats Library for Machine Learning

math4ml is a modular, NumPy-backed Python library designed to teach, visualize, and compute the mathematics behind AI & Machine Learning.

It combines:

• Linear algebra
• Statistics
• Probability
• Hypothesis testing
• Preprocessing
• Visualizations
• Educational examples

with NumPy speed, Numba optimizations, and Khan-Academy–style explanations.

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🚀 Features

1. Linear Algebra

• Matrix operations: matmul, add, subtract, transpose, inverse, det, …
• Vector operations: dot, norm, angle, projection, …
• Decompositions: LU, QR, SVD (optional upgrade)
• Interactive visualizations for matrix multiplication, dot products, transformations, etc.

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2. Statistics

• Descriptive stats: mean, var, std, median, range
• Correlation: Pearson, Spearman
• Distributions: normal, binomial, uniform, Poisson
• Hypothesis tests:
  • t-test
  • chi-square test
  • ANOVA
  • z-test
  • non-parametric tests (coming soon)

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3. Probability

• PMF, PDF, CDF utilities
• Combinatorics: nCr, nPr
• Bayes theorem helpers
• Random variable simulation utilities

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4. Preprocessing

• Scaling:
  • StandardScaler
  • MinMaxScaler
  • MaxAbsScaler
  • RobustScaler
• Encoding:
  • One-hot
  • Label
  • Binary
• Feature engineering helpers

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5.optimization

6.ml_models

-classification_models -"LogisticRegression", -"NaiveBayes", -"KNN" -linear_models -"LinearRegression", -"RidgeRegression", -"LassoRegression" -metrics -"RegressionMetrics", -"ClassificationMetrics" -validation -"CrossValidation"

7. Educational Tools

Every function includes:

• 🧮 Mathematical formula
• 📘 Concept explanation
• 🔍 Assumptions
• ✏️ Step-by-step example
• 📓 Jupyter notebook tutorials

Perfect for students learning ML math, data scientists, and AI researchers.

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📦 Installation

PyPI

pip install math4ml


**🧠 Quickstart Example**
just use print(math4ml.linalg.__doc__), print(math4ml.__doc__) or help(math4ml)

from math4ml.linalg import matmul
from math4ml.stats import t_test

print(matmul([[1, 2]], [[3], [4]]))

stat, p = t_test([1,2,3], [3,4,5])
print("T-stat:", stat, "P-value:", p)

📚 Tutorials
🔍 Explore: https://github.com/SANJAYRAM-DS/math4ml.tutorials.git

Contains:

-Linear algebra examples

-Statistical tests

-Probability examples

-Preprocessing tutorials

-optimization

-ml_models

🤝 Contributing

We welcome contributions from everyone!

You can help by:

-🐛 Reporting issues

-🌟 Suggesting features

-📘 Improving documentation

-🧪 Adding tests

-🧩 Adding examples

-🔧 Submitting pull requests

📝 License

MIT License — free for commercial, educational, and research use.