# Introduction # This tutorial provides guidance on gathering data through web-scraping. However, to demonstrate the real-life issues with acquiring data, a deep-dive into a specific, complicated example is needed. The problem chosen, acquiring the geographic coordinates of gas stations in a region, turns into an interesting math problem that, eventually, involves "sacred geometry". ## Application Programming Interfaces (API's) ## No matter which Data Science Process [model](https://www.kdnuggets.com/2016/03/data-science-process-rediscovered.html) you subscribe to, actually acquiring data to work with is necessary. By far the most straightforward data source is a simple click-to-download in a standardized file format so you can utilize a parsing …

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# What Are Voronoi Diagrams? # Voronoi Diagrams are an essential visualization to have in your toolbox. The diagram’s structure is a data-driven [tessellation](https://en.wikipedia.org/wiki/Tessellation) of a plane and may be colored by random or to add additional information. ## Learn the Lingo ## The set of points that generate the Voronoi diagram are called “seeds” or “generators” for they generate the polygon shapes; in practice, the seeds are your cleaned data. Each seed generates its own polygon called a Voronoi “cell,” and all of 2-dimensional (2D) space is associated with only one cell. A Voronoi cell, or Voronoi “region” denotes …

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## Intro ## Receiver Operating Characteristic (ROC) plots are useful for visualizing a predictive model’s effectiveness. This tutorial explains how to code ROC plots in Python from scratch. ## Data Preparation & Motivation ## We’re going to use the breast cancer dataset from sklearn’s sample datasets. It is an accessible, binary classification dataset (malignant vs. benign) with 30 positive, real-valued features. To train a logistic regression model, the dataset is split into train-test pools, then the model is fit to the training data. from sklearn.datasets import load_breast_cancer from sklearn.linear_model import * from sklearn.model_selection import train_test_split # Load datasetd dataset = …

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What has Schrodinger got to do with the complex and paradoxical concept of quantum computing? The answer is that both Erwin and his hypothetical cat relate directly to this subatomic world. In fact, Schrodinger is often referred to as the father of quantum mechanics. ## Erwin who? ## [Erwin Schrodinger](https://en.wikipedia.org/wiki/Erwin_Schr%C3%B6dinger) was an Austrian theoretical physicist, awarded a Nobel Prize way back in 1933 for his work with Paul Adrien Maurice Dirac regarding the discovery of 'new productive forms of atomic theory.' Two years later, Schrodinger proposed an experiment whereby a hypothetical cat and a radioactive sample are sealed within a …

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In this tutorial I am going to go over a brief history of artificial intelligence. Let's begin. **The Beautiful Equation** ![a0ac936120a8fa20eba9c2f4891766f5](/attachments/large/0/a0ac936120a8fa20eba9c2f4891766f5.png "a0ac936120a8fa20eba9c2f4891766f5") OK I agree, it's a bit heavy to begin with but towards the end we will see why the above equation is, in my opinion, of great importance to the field of AI. But first let's go back in time to ancient Greece. **Ancient Greece** The Greeks dreamt about artificial intelligence, playing God if you will, they probably weren't the first. But the idea of mechanically creating an intelligence to rival our own has always been on the …

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There is little doubt that one of the most daunting tasks in all of computer programming is that of developing your own algorithms. Indeed, it is here where the term Computer Science comes to the fore as it is virtually a step-by-step process, so intricate and precise it truly is a science to master. So what exactly are algorithms? Simply put, these are step-by-step methods which a programmer tells a computer to follow. They often branch out under certain conditions, in the same way that as humans with brains we follow trillions of algorithms in our daily lives although you …

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The End.