Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.

A library providing modules with easy to use matrix operations for real(8) and complex(8) arrays

Imperial College London »Mathematics for Machine Learning«. A sequence of 3 courses on the prerequisite mathematics for applications in data science and machine learning. (1) Linear Algebra (2) Multivariate Calculus and (3) Principal Component Analysis (completed Sept. 10th, 2018)

ForEig - A Fortran library for eigenvalue and eigenvector calculations.

This MATLAB program is designed to calculate eigenvalues and eigenvectors of a square matrix provided by the user. Eigenvalues and eigenvectors are fundamental concepts in linear algebra and have various applications in mathematics, science, and engineering.

Linear Algebra Step by Step by Kuldeep Singh. Solved step by step through LaTeX

A PyGame simulation of the motion over time for an interconnected mass-spring-system with n masses

Lanczos program for large sparse Hermitian matrices (Fortran 95 with C99 interface)

Fortran 90/95 adaptation of a couple of EISPACK Fortran 77 routines

A small portable C library with several utility functions.

Computational Linear Algebra - Python

This repository contains a Python implementation of Principal Component Analysis (PCA) for dimensionality reduction and variance analysis. PCA is a powerful statistical technique used to identify patterns in data by transforming it into a set of orthogonal (uncorrelated) components, ranked by the amount of variance they explain.

This repository contains all the quizzes and assignments required to complete all 3 courses of the Mathematics for Machine Learning Specialization on Coursera

In this project, I've used College/University data to perform Principal Component Analysis and provide its implications to the business

DCGeig is a solver for large, sparse generalized eigenvalue problems with real symmetric positive definite matrices. It computes eigenvalues and eigenvectors and can be used, e.g., for computing eigenfrequencies of finite element models.