## Hacking Through Mathematics

Same guide for hacking in statistics. Here are the details about how I managed to hack through mathematics. Denis Sheeran takes on the problems created through using many of the traditional math lesson routines and provides the reader with easy to implement ...

## What are the differences between generative and discriminative machine learning models?

Generative and discriminative are classes of machine learning models. In this article, we will look at the difference between generative and discriminative models, how they contrast, and one another.

## A crash course on floating point numbers in a computer

Unlike integers, the floating point number system in a computer doesn’t use two’s complement to represent negative numbers. What are the numeric data types in a computer programming language? That is one of the first things a beginner is taught. In most cases, the ...

## Efficient Permutations In Lexicographic Order

I will show you a method to generate sorted permutations in lexicographic order. We’ll see a method to find the nth permutation efficiently.We'll generate permutations in lexicographic order. Also, we'll see a method to obtain an ordered permutation at an arbitrary position efficiently.

## 68–95–99 Rule — Normal Distribution Explained in Plain English

The 68–95–99 Rule — Normal Distribution Explained in Plain English. It’s a foundational concept in statistics, and the key to understanding a range of natural phenomena

## Hypothesis Test for Real Problems

Hypothesis tests are significant for evaluating answers to questions concerning samples of data. In this article, you can explore a type of hypothesis, why we need it, and how to calculate it?

## Understanding and Choosing the Right Probability Distributions with Examples

The Most Common Discrete Probability Distributions Explained with Examples. The correct discrete distribution depends on the properties of your data. For example, use the: Binomial distribution to model binary data, such as coin tosses.

## A Small Problem With Big Numbers

A Small Problem With Big Numbers. The human mind is exceptionally bad at interpreting large numbers.

## Where Mathematics Being Used Commonly?

Since it’s more than 4000 years old, it has created numerous wonders and creates more in our lives. Here, we’ll outline where mathematics is used in today’s world.

## Why You Shouldn’t Go to Casinos (3 Statistical Concepts)

Because of 3 simple statistical concepts: survivorship bias. expected value. and hot-hand fallacy.The house always wins. We all know this phrase. But this is more than a phrase. This is a simple, mathematically proven fact. And you’ll only have to know three statistical concepts to see why the house always wins.

## Differentiable Programming from Scratch

In this article, we are going to explain what Differentiable Programming is by developing from scratch all the tools needed for this exciting new kind of programming.

## Famous Probability Distributions in Data Science

Famous Probability Distributions in Data Science. Probability Distributions allow a Data Scientist or Data Analyst to recognize patterns in any case totally random variables.

## The Math behind Machine Learning

In this article, I will try to cover a simple yet effective part of the math behind machine learning which is the probabilistic view of models using probability simple rules only.

## How to add Fourier terms to your regression & seasonality analysis (using Python & SciPy)

How to add Fourier terms to your regression & seasonality analysis (using Python & SciPy). Include seasonality in your regressions by using Fourier terms

## Integrals are Fun: Illustrated Riemann-Stieltjes Integral

Integrals are Fun: Illustrated Riemann-Stieltjes Integral. Illustrated examples of the Riemann-Stieltjes Integration in Python with Matplotlib.

## Different Types of Time Series Decomposition

This article has the following goals: Explain the importance of time series decomposition; Explain the problems with the seasonal_decompose function; Introduce alternative approaches to time series decomposition.

## What is the Bayesian Theorem?

What is the Bayesian Theorem? Bayesian is helpful in the feeling of uncertainty for decision making. Bayesian probability permits one to quantify the uncertainty.

## Make Causation the Goal for your Next Data Science Project

A case study with money and interest rates. I will (try) to prove a causal relationship between interest rates and the money supply.

## Univariate and Multivariate Gaussian Distribution: Complete Understanding with Visuals

Gaussian distribution in details and its relationship with mean, standard deviation, and variance. In this article, I cut some of the visuals from his course and used it here to explain the Gaussian distribution in detail.

## You’re Probably Misusing the P-Value

And Why I don’t Care If your p-value is Less than 0.05. The goal for this article is to clear up the myths surrounding the p value, and hopefully, encourage data scientists to look beyond the p value in their own projects.