# Tag: Bayes Theorem (14)

**10 Must-Know Statistical Concepts for Data Scientists**- Apr 21, 2021.

Statistics is a building block of data science. If you are working or plan to work in this field, then you will encounter the fundamental concepts reviewed for you here. Certainly, there is much more to learn in statistics, but once you understand these basics, then you can steadily build your way up to advanced topics.**10 Statistical Concepts You Should Know For Data Science Interviews**- Feb 23, 2021.

Data Science is founded on time-honored concepts from statistics and probability theory. Having a strong understanding of the ten ideas and techniques highlighted here is key to your career in the field, and also a favorite topic for concept checks during interviews.**KDnuggets™ News 21:n05, Feb 3: How to Get a Job as a Data Scientist; Popular Machine Learning Interview Questions, part 2**- Feb 3, 2021.

Learn how to get a job as Data Scientist; it will help if you study popular machine learning interview questions; Beyond the Nash Equilibrium: DeepMind Clever Strategy to Solve Asymmetric Games; Understanding Bayes Theorem; and more.**3 Ways Understanding Bayes Theorem Will Improve Your Data Science**- Feb 1, 2021.

Mastery of the mathematics and applications of this intuitive statistical concept will advance your credibility as a decision maker.**Naïve Bayes Algorithm: Everything you need to know**- Jun 8, 2020.

Naïve Bayes is a probabilistic machine learning algorithm based on the Bayes Theorem, used in a wide variety of classification tasks. In this article, we will understand the Naïve Bayes algorithm and all essential concepts so that there is no room for doubts in understanding.**Probability Learning: Naive Bayes**- Nov 26, 2019.

This post will describe various simplifications of Bayes' Theorem, that make it more practical and applicable to real world problems: these simplifications are known by the name of Naive Bayes. Also, to clarify everything we will see a very illustrative example of how Naive Bayes can be applied for classification.**The Math Behind Bayes**- Nov 19, 2019.

This post will be dedicated to explaining the maths behind Bayes Theorem, when its application makes sense, and its differences with Maximum Likelihood.**How Bayes’ Theorem is Applied in Machine Learning**- Oct 28, 2019.

Learn how Bayes Theorem is in Machine Learning for classification and regression!**Probability Learning: Bayes’ Theorem**- Oct 16, 2019.

Learn about one of the fundamental theorems of probability with an easy everyday example.**When Bayes, Ockham, and Shannon come together to define machine learning**- Sep 25, 2018.

A beautiful idea, which binds together concepts from statistics, information theory, and philosophy.**How Bayesian Inference Works**- Nov 15, 2016.

Bayesian inference isn’t magic or mystical; the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer. Read an in-depth overview here.**History of Data Mining**- Jun 22, 2016.

Data mining is a subfield of computer science which blends many techniques from statistics, data science, database theory and machine learning. Here are the major milestones and “firsts” in the history of data mining plus how it’s evolved and blended with data science and big data.**Bayes Theorem for Computer Scientists, Explained**- Feb 16, 2016.

Data science is vain without the solid understanding of probability and statistics. Learn the basic concepts of probability, including law of total probability, relevant theorem and Bayes’ theorem, along with their computer science applications.**Top KDnuggets tweets, Feb 26 – Mar 1: Bayes Theorem explained with Lego; 10 Cool #BigData Cartoons**- Mar 2, 2015.

Cute and Educational: Bayes Theorem explained with Lego; 10 Cool #BigData Cartoons #TGIF; #DataMining Indian Recipes finds spices make negative food pairing more powerful; Key Take-Aways from Gartner 2015 MQ for #BI & Analytics Platforms.