Introduction to Nonparametric Statistical Significance Tests in Python

In applied machine learning, we often need to determine whether two data samples have the same or different distributions. We can answer this question using statistical significance tests that can quantify the likelihood that the samples have the same distribution. If the data does not…

FogHorn Receives Industry Recognition for Artificial Intelligence and Machine Learning in Industrial IoT

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A Neural Network Model That Can Reason – Prof. Christopher Manning – YouTube

Published on May 14, 2018 Christopher Manning is the inaugural Thomas M. Siebel Professor in Machine Learning in the Departments of Computer Science and Linguistics at Stanford University. Recorded May 3rd, 2018 at ICLR2018 Category License Standard YouTube License

Artificial Intelligence in Food & Beverages: Global Market Forecasts 2018-2023 – ResearchAndMarkets.com

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TraceLink Adds Industry Visionaries to Discuss Machine Learning, Artificial Intelligence, Big Data and Patient Engagement at FutureLink Munich

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Statistics Books for Machine Learning

Statistical methods are used at each step in an applied machine learning project. This means it is important to have a strong grasp of the fundamentals of the key findings from statistics and a working knowledge of relevant statistical methods. Unfortunately, statistics is not covered…

A Gentle Introduction to the Central Limit Theorem for Machine Learning

The central limit theorem is an often quoted, but misunderstood pillar from statistics and machine learning. It is often confused with the law of large numbers. Although the theorem may seem esoteric to beginners, it has important implications about how and why we can make…

Introduction to Random Number Generators for Machine Learning in Python

Randomness is a big part of machine learning. Randomness is used as a tool or a feature in preparing data and in learning algorithms that map input data to output data in order to make predictions. In order to understand the need for statistical methods…