This repository contains tutorials on the introductory mathematical concepts required for studying statistics and machine learning.
The primary aim of this repository is to help data scientists to revise the mathematical concepts required for understanding machine learning and statistics.
This repository also has a secondary aim of promoting code agnostic practices, by highlighting how the same functions can be performed in both R and Python using the R reticulate package.
| Topics | Tutorials |
|---|---|
| 🔢 | Introduction to numbers |
| 🔢 | Introduction to algebra |
| 🔢 | Exponents and logarithms |
| 🔢 | Logarithms and information theory |
| 🃏 | Introduction to probability theory |
| 🃏 | Conditional probability |
| 🃏 | Bayes theorem |
| 🍪 | Introduction to summations |
| 🧭 | Introduction to trigonometry |
| 🧭 | Distance metrics |
| 🧭 | Cosine similarity |
| 🥢 | Introduction to vectors |
| 🥢 | Vector transformations |
| 🥢 | Vector embeddings |
| 🏬 | Introduction to matrices |
| 🎢 | Introduction to derivatives |
- A guide to linear algebra for applied machine learning by Pablo Caceres
- The Mathematics for Machine Learning textbook by Marc Peter Deisenroth, A Aldo Faisal and Cheng Soon Ong - Cambridge University Press
- The Probability for Data Science textbook by Stanley H Chan - Michigan Publishing
- The Probabilistic modelling tutorials by Michael Betancourt - GitHub
- Writing mathematical operations in LaTex/R - Wikibooks