Mathematics for Machine Learning Specialization

Detailed notes and exercises for the "Mathematics for Machine Learning" specialization of Imperial College London.

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)

QSD's MVC study.

This repository contains a comprehensive collection of mathematical concepts and techniques relevant to various fields of AI, including ML, DL & other areas.It also includes the corresponding source code for all programming tasks associated with the Mathematics for Machine Learning courses, which are taught at Coursera by Imperial College London.

Semester-2 | A repository that is a complete archive of my second semester coursework. The goal is to keep everything from the semester backed up and organized in one place — so that all my work is safe.

Code to visualize function F(x,y) in 3D space, heat map, contour lines along with line integral visualization

Mathematics for Machine Learning is a specialisation in which you will master the fundamental mathematical toolkit for machine learning: calculus, linear algebra, statistics, and probability.

Solutions to Multivariable Calculus by Don Shimamoto | Solved step by step through LaTeX

Interactive two variable function grapher

Interactive 3D mathematical multivariable function visualizer with critical point detection. Built with C++ and raylib.

Multivariable Calculus Final Project

A calculus focused computer algebra system in Rust.

MCLA notes

My exploration into Multivariable Calculus translated to code.

ICL's Mathematics for Machine Learning specialization, Python assignments.

Public versions of my manuscripts.

3d Vector Field Art Tool

Effective HP calculator turned multivariable regression experiment