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##Machine Learning with Scikit
###Setup and Installation
For this tutorial we will be working with a Python framework called Scikit Learn. This is a free machine learning library that will allow us to execute multiple ML techniques and methodologies.
The only way I've succeeded so far is to employ SSH.
Assuming you are new to this like me, first I'd like to share with you that your Mac has a SSH config file in a .ssh directory. The config file is where you draw relations of your SSH keys to each GitHub (or Bitbucket) account, and all your SSH keys generated are saved into .ssh directory by default. You can navigate to it by running cd ~/.ssh within your terminal, open the config file with any editor, and it should look something like this:
This is just learning journal for myself and I welcome any help from the public to improve my understanding.
Whether a globally or locally installed module, eventually it will reside within a node_modules directory. The contents of this gist is scopped within the local node_modules of an application's directory. Now within the local node_modules there should always exist a .bin folder that houses all the excutable files.
What I've gathered thus far:
On Unix/macOs, this files have the chmod 755 or 777 permissions to run as scripts.
All of these files start with #!/usr/bin/env node on the very first line.
top command will show a list of all running processes and various statistics about each process. It’s usually most helpful to sort by processor usage or memory usage, and to do that you’ll want to use the -o flag
top -o cpu | grep :YOUR_PORT_NUMBER shows details of the respective pid