Our default base is where we have installed Anaconda.

• Go to the directory you wish to create your environment using Terminal.
  • cd <YourPath>
  • conda create --prefix ./env <tools You need>
  • conda install <your tool>

As an Example :

$ cd /d G:\GitRepositories\NewEnvironment
$ conda create --prefix ./env pandas matplotlib numpy scikit-Learn
$ conda activate
$ conda install jupyter

• Firstly you should build a Conda Environment
• Open Terminal
• cd to the directory that contains the built environment.

cd /d <path>
Ex : cd /d G:\GitRepository\Machine-Learning\

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• Activate Anaconda Environment simply by typing : $ conda activate [environmentName]
  • If you don't specifically name the [environmentName] name , it will automatically open the base environment. =======
• Acitvate Anaconda Environment simply by typing : $ conda activate [environmentName]
  • If you don't specifictly name the [environmentName] name , it will automatically open the base environment.
  • You can see the existing environments by simply typing conda info --env

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• Open Jupyter Notebook : $ jupyter notebook

As an Example :

$ cd /d G:\GitRepository\Machine-Learning/
$ conda activate RegEnv
$ jupyter notebook

$ cd /d G:\GitRepositories\NewEnvironment
$ conda create --prefix ./env pandas matplotlib numpy scikit-Learn
$ conda activate
$ conda install jupyter
$ jupyter notebook

• Press Esc
• Press m on the keyboard

• Press Esc
• Press y on the keyboard

• Press Esc
• Press x on the keyboard

• Shift + Enter

• Go to the paranthesis () of the function
• push shift + tab to see the function's documentation

• Only Tab ... push multiple times to show the options.

This repository is released under Apache 2.0 License. To put it in a nutshell, This means that you can use the source codes in any opensource projects or contributions. Attribution is not mandatory but appreciated.

To remove all barriers in the way of science.