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Fundamentals of Data Mining

Data Science 101

Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). The patterns discovered after this step are interpreted using various visualization and reporting techniques and are made comprehensible for other team members to understand. Deployment.

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On the Hunt for Patterns: from Hippocrates to Supercomputers

Ontotext

Ever since Hippocrates founded his school of medicine in ancient Greece some 2,500 years ago, writes Hannah Fry in her book Hello World: Being Human in the Age of Algorithms , what has been fundamental to healthcare (as she calls it “the fight to keep us healthy”) was observation, experimentation and the analysis of data. Certainly not!

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The Semantic Web: 20 Years And a Handful of Enterprise Knowledge Graphs Later

Ontotext

If you’ve used Google, you’ve used the cornucopia of Linked data across the Web, through Google’s Knowledge Graph (Google’s Knowledge Graph is reportedly supported by Freebase – the knowledge acquired by Google in 2010. ) What Does The Semantic Web Already Do for Us?

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Using Empirical Bayes to approximate posteriors for large "black box" estimators

The Unofficial Google Data Science Blog

For an introduction to Empirical Bayes, see the paper [3] by Brad Efron (with more in his book [4]). References [1] Omkar Muralidharan, Amir Najmi "Second Order Calibration: A Simple Way To Get Approximate Posteriors" , Technical Report, Google, 2015. [2] In Figure 2, the red line shows a Gamma prior that leads to a good fit.

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