A structured repository covering essential mathematics for machine learning, with clear explanations, intuition, and Python implementations.
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Updated
Mar 24, 2026 - Jupyter Notebook
A structured repository covering essential mathematics for machine learning, with clear explanations, intuition, and Python implementations.
A beginner-friendly collection of Jupyter notebooks covering the essentials of linear algebra with clear NumPy code and visualizations. Topics include vectors, basis, transformations, dot product, determinants, eigenvalues/eigenvectors, matrix inverse, rank, and cross product—ideal for students and self-learners.
production ready implementation of Linear Regression using only NumPy, demonstrating the mathematical foundations from gradient descent to scalable deployment.
Implementing Linear and Logistic Regression from Scratch using NumPy
A NumPy-only Multilayer Perceptron (MLP) built from first principles to demystify neural networks, backpropagation, and optimization algorithms.
Pure Python & NumPy implementations of core supervised and unsupervised machine learning algorithms built from first principles.
the machine learning implementation with maths under the hood of algorithm
A structured collection of notes covering the mathematics, classical algorithms, and neural-network concepts that form the foundations of machine learning.
Quick-reference notes and Python implementations for the foundational mathematics (Linear Algebra, Calculus, Probability & Statistics) powering Machine Learning, AI, and Data Science.
Curated roadmap and free resources to learn mathematics for machine learning: Linear Algebra, Calculus, Probability, Statistics, and Optimization with Python code examples.
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