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The Art of Machine Learning (Algorithms + Data + R)

The Art of Machine Learning (Algorithms + Data + R)

I wrote this book because: • ML is not a recipe. It is not a matter of knowing the syntax and mechanics of various software packages.• ML is an art, not a science. (Hence the title of this book). • One does not have to be a math whiz or know advanced math in orer to use ML effectively, but one does need to understand the concepts well — the Why? and How? of ML methods

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Microsoft NLP Best Practices

Microsoft NLP Best Practices

This repository contains examples and best practices for building NLP systems, provided as Jupyter notebooks and utility functions. The focus of the repository is on state-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language

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Trainings for Cybersecurity Specialists

Trainings for Cybersecurity Specialists

«ENISA CSIRT training material was introduced in 2008. In 2012, 2013 and 2014 it was complemented with new exercise scenarios containing essential material for success in the CSIRT community and in the field of information security. In these pages you will find the ENISA CSIRT training material, containing Handbooks for teachers, Toolsets for students and Virtual Images to support hands on training sessions. » The materials continue to be updated in 2020 and are appropriate for use by cybersecurity specialists and decision-makers.

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EUROPEAN CYBER SECURITY CHALLENGE 2020 (Change dates)

EUROPEAN CYBER SECURITY CHALLENGE 2020 (Change dates)

(Change of dates to 2021).Top cyber talents from each participating country will meet in Vienna to network and collaborate and finally compete against each other. Contestants will be challenged in solving security related tasks from domains such as web security, mobile security, crypto puzzles, reverse engineering and forensics and in the process collect points for solving them.

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Best Practices in Dataviz: An R Perspective

Best Practices in Dataviz: An R Perspective

By the end of this you will have had a whirlwind tour of the very tip of the data visualization best-practices iceberg. We will go over a broad range of topics generally applicable to data science usecases but not dive too deep into any single one. One thing to keep in mind the whole time is none of this is absolutely set in stone, most often in the real world you have to bend or break some of these rules to do what you want.

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