Check out this article by Daniel Bourke. Simplifies things so much, either (if you want to) read through it or watch the video and take it step-by-step (though you will still have to Google if you have installation errors ) . I mainly use my Windows machine for ML… makes me want to install miniconda on my Mac, which has lower specs and is nearing 6 years old! I think I will do so when I have more free time and am not drowned with a ton of school work!
Looks like a great guide for getting started! I personally just use miniconda + command line on all my machines. Anaconda feels too bloated. Although if you are starting out the whole package provided by Anaconda might actually make things easier for you.
Either way, getting yourself familiar with virtual environments is in my eyes something very important for soon-to-be data scientists with an intermediate skill level!
Yep true agreed @htw !
Thanks for sharing @masterryan.prof! It’s a nice guide to read.
But still, my doubt remains: should I set up an environment for each project? Or I can have multiple environments for different project types (like data cleaning, data viz, machine learning, web scraping) and so on?
@artur.sannikov96: My suggestion is to create many envs only if you have incompatibility issues on installation. Otherwise of course it better to install everything in one env so that you don’t have to keep switching to access various packages.