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You have created your first Linear Regression Model. Have you validated the assumptions?
Linear Regression is an excellent starting point for Machine Learning, but it is a common mistake to focus just on the p-values and R-Squared values while determining validity of model. Here we examine the underlying assumptions of a Linear Regression, which need to be validated before applying the model.
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How Bayesian Networks Are Superior in Understanding Effects of Variables
Bayes Nets have remarkable properties that make them better than many traditional methods in determining variables’ effects. This article explains the principle advantages.
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What is the difference between Bagging and Boosting?
Bagging and Boosting are both ensemble methods in Machine Learning, but what’s the key behind them? Here we explain in detail.
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More than the Hype: Beyond Gartner’s Hype Cycle
Gartner publishes hype cycles across different technologies and sectors. Here we conduct detailed analysis of Gartner’s Hype Cycles.
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Process Mining with R: Introduction
In the past years, several niche tools have appeared to mine organizational business processes. In this article, we’ll show you that it is possible to get started with “process mining” using well-known data science programming languages as well.
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Top 6 errors novice machine learning engineers make
What common mistakes beginners do when working on machine learning or data science projects? Here we present list of such most common errors.
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The danger in comparing your campaign performance against an average
Performance measurement is only meaningful when compared against a benchmark. While “average” is a good, and easy to understand metric, it could be very deceptive.
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Business intuition in data science
Data Science projects are not just use of algorithms & building models; there are other steps of the project which are equally important. Here we explain them in detail.
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Rethinking 3 Laws of Machine Learning
We rethink Asimov’s 3 law of robotics to help companies moving to unsupervised machine learning and realize 100% automated predictive information governance (PIG).
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Edge Analytics – What, Why, When, Who, Where, How?
Edge analytics is the collection, processing, and analysis of data at the edge of a network either at or close to a sensor, a network switch or some other connected device.
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