Benford’s law, often known as Newcomb-Benford law, is an observation about the frequency distribution of leading digits of unconstrained numeric data in the real world. The intuition behind the law dates to the 1880s when an American Scientist, Simon Newcomb started to discover a pattern among the log tables. He noticed that people usually have…

Central Limit Theorem -Complete Guide for Beginners! 1) There is a population. 2) We take many number of random samples of same size from our population. 3) Take mean of those samples. 4) Plot those mean on distribution graph ( Histogram )

Wasserstein Distance, Contraction Mapping, and Modern RL Theory. Classic Math Formulations And Their Impact on Modern RL ern RL

The first two figures show what can happen — they are extreme cases, and other cases may incorporate both small continuous changes and big jumps. The third figure below indicates what “the picture becomes clearer” analogy would suggest — that probabilities should change only by moving closer to the ultimate outcome.

Why is the Central Limit Theorem Important to Data Scientists? The Central Limit Theorem is at the center of statistical inference what each data scientist/data analyst does every day.

The Central Limit Theorem (CLT) is one of the most popular theorems in statistics and it’s very useful in real world problems. In this article we’ll see why the Central Limit Theorem is so useful and how to apply it.

Uncertainty utilizing the tools of Probability. Machine learning is tied in with creating predictive models from uncertain data. Uncertainty utilizing the tools of probability.

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.

I aim to cover 5 different probability questions (increasing in difficulty) which I believe serve as a good blanket to the various types of questions you would expect in the interview process.

Foundations of Probability Theory & a few proofs. These three axioms form the foundations of Probability Theory, from which every other theorem or result in Probability can be derived.

This article will introduce the seven most important statistical distributions, show their Python simulations with either the Numpy library embedded functions or with a random variable generator, discuss the relationships among different distributions and their applications 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.

Oh, the Places You’ll Go in Monopoly. Exploring steady-state probabilities with Markov chains

Text Classification Using Naive Bayes: Theory & A Working Example. In this article, I explain how the Naive Bayes works and I implement a multi-class text classification problem step-by-step in Python.

Today we are introducing a new series solving Data Science interview questions at these same companies. We will solve three simple problems asked at Facebook for a Data Science role.

To understand and grasp the core concepts behind some of the most prominently used Machine Learning algorithms, it is important that one is at least familiar with the basics of Statistics and Probability. The aim of this article is to give you a valuable introduction to Probability and its various types

Why and when should you calibrate the probabilities of a classifier. I will give a general introduction to probability calibration for classifiers and discuss when it makes sense to use calibrators.

The Best Free Data Science eBooks: 2020 Update. The author has updated their list of best free data science books for 2020. Read on to see what books you should grab.

Estimate Pi With Monte Carlo. Example of how you can estimate Pi With Monte Carlo in four lines of code

This article aims to: Mathematically specify the definitions for RVs being uncorrelated, and RVs being independent.Prove that RVs that are independent are by definition also uncorrelated. Prove that RVs can be uncorrelated but not independent (by example)