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Data Science Blog

Machine learning & data science for beginners and experts alike.
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Community Content Engineer
Community Content Engineer

BRAINS.PNG

Neural Networks are an approach to artificial intelligence that was first proposed in 1944.  Modeled loosely on the human brain, Neural Networks consist of a multitude of simple processing nodes (called neurons) that are highly interconnected and send data through these network connections to estimate a target variable. In this article, I will discuss the structure and training of simple neural networks (specifically Multilayer Perceptrons, aka "vanilla neural networks"), as well as demonstrate an example neural network created by the Alteryx Neural Network Tool.

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Alteryx
Alteryx

LINEAR REGRESSION.png

Building my first linear regression model turned me into an instant celebrity. My roommate, who has acted as a sounding board for my predictive-analytics-learning progress, now believes I can use Linear Regression to predict the winner of the next horse race. While it would be fun to try, a more applicable use case is predicting how much a customer will spend (which, in the case of horse racing could translate to how much someone might spend on a bet). For my use case, I want to predict how much a Lyft driver can expect to receive on their next fare.

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Alteryx
Alteryx

decisionTree.png

New to Alteryx and ready to help improve the documentation, I'm clambering up another predictive suite tool: the decision tree.

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Alteryx
Alteryx

 

flag_1.pngWhat is support vector machine and why should you use it?

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Alteryx Alumni (Retired)

 

random-forest-3.pngAn introduction to working with random forests using Python

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Alteryx
Alteryx

 

father-son-regression.pngA high level introduction to what linear regression is and how it works.

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Alteryx Alumni (Retired)


As a fairly new developer on the Core Engines team, I am thrilled to have the opportunity of adding functionality to the R Tool. 

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