# Math For Machine Learning

## This is all the available math you need for Machine Learning. In this section, you will learn Linear Algebra, Statistics, Probability Theory and Calculus. Linear Algebra

## Inverting A Matrix: Gaussian Elimination & Row Echelon Form

Machine Learning algorithms make use of matrix inverses, but understanding how to invert a matrix is not easy. We walk through the theory and show you how to invert a matrix by example. Statistics

## How to do Linear Regression and Logistic Regression in Machine Learning?

Wondering how Linear Regression or Logistic Regression works in Machine Learning? Python code and a walkthrough of both concepts are available here. Probability

## Probability Distributions and Maximum Likelihood Estimation (MLE)

Probability distributions is all about how we can represent the distributions of probabilities of data. There is fundamentally a formula for each distribution, but I like to visualize the sort Probability

## Probability Theory: Bayes Theorem, Sum Rule and Product Rule

This post is where you need to listen and really learn the fundamentals. All modern approaches to Machine Learning uses probability theory. AlphaStar is an example, where DeepMind made many Statistics

## Statistics Basics - Variance and Standard Deviation

We want to be able to say something about our data using some statistical measures, because it is important information. There are many ways to use statistics in machine learning, Linear Algebra

## Cheat Sheet for Linear Algebra

This is a continuously updated cheat sheet for the Linear Algebra I covered, as well as for future posts. Currently included are intuition, notation and formulas. Notation like vector, scalar, matrix, m x n, basis vectors, mapping in space, determinant, cross product, dot product and much more. Linear Algebra

## Linear Algebra Basics 4: Determinant, Cross Product and Dot Product

I visualized the determinant, cross product and dot product can be hard. Come read the intuitive way of understanding these three pieces from Linear Algebra.  