Machine Learning Introduction: Setting Up Python for Machine Learning

Victor Roman
4 min readDec 10, 2018

This is the second article of the Machine Learning series. If you want to know what is Machine Learning and what are the different types of problems and algorithms, check the first part.

In this article we are going to explore why Python is currently the most popular programming language for Machine Learning and how to set up the perfect environment for developing your projects.

Picture from Unsplash

Why Python?

Created in 1991 by Guido van Rossum, Python is now the fastest growing programming language among all, and the trend is only expected to keep the pace.

Philosophy

The reasons why is being accepted and used at such an impressive pace can be explained by taking a look at its philosophy (Tim Peters-2004):

  • Beautiful is better than ugly.
  • Explicit is better than implicit.
  • Simple is better than complex.
  • Complex is better than complicated.
  • Flat is better than nested.
  • Sparse is better than dense.
  • Readability counts.
  • Special cases aren’t special enough to break the rules.
  • Although practicality beats purity.
  • Errors should never pass silently.
  • Unless explicitly silenced.
  • In the face of ambiguity, refuse the temptation to guess.
  • There should be one — and preferably only one — obvious way to do it.
  • Although that way may not be obvious at first unless you’re Dutch.
  • Now is better than never.
  • Although never is often better than right now.
  • If the implementation is hard to explain, it’s a bad idea.
  • If the implementation is easy to explain, it may be a good idea.
  • Namespaces are one honking great idea — let’s do more of those!

This manifesto yields in the features that set apart Python from other languages.

Python Features

  1. Readable

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Victor Roman

Industrial Engineer and passionate about 4.0 Industry. My goal is to encourage people to learn and explore its technologies and their infinite posibilites.