Setting Up a Python Machine Learning Environment

With the explosion of Machine Learning, Artificial Intelligence, and Neural Networks recently, it can be difficult to decern how to get started learning all this fancy new tech. It can...

a year ago

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With the explosion of Machine Learning, Artificial Intelligence, and Neural Networks recently, it can be difficult to decern how to get started learning all this fancy new tech. It can even be difficult to figure out how to set up your environment correctly to facilitate this learning with the majority of development being done in Python and R. This article shows how to get a basic setup going for both languages by using Anaconda, SciPy, and Tensorflow.

Prerequisites

Note: This setup will work on Linux, Windows 10 or MacOS but this article demonstrates the setup using MacOS so some of the installer options/commands may differ on other operating systems

Anaconda-Navigator

If you have not already installed Anaconda-Navigator go ahead and do so. Anaconda is an industry standard for developing data science applications such as AI or machine learning applications. It can take advantage of either Python or R as its language of choice and allows users to easily configure their environments through a GUI or command line tool.

After you get Anaconda-Navigator installed launch it using the Launchpad (CMD + Space for a shortcut on MacOS) and then click on the environments tab.

Launch Anaconda-Navigator

This screen will show all your configured environments, the installed packages for each environment, and allows you to create and clone. Click on the base (root) environment, then the green arrow icon, and finally Open Terminal to launch a terminal session with the selected environment loaded.

Open Terminal

Update SciKit-Learn

Now we need to update some of the existing libraries and add a few new ones.

conda update conda
conda update anaconda

These two commands will use the conda CLI to update itself, and then the anaconda package to make sure everything already installed is up to date.

conda update scikit-learn

This command will update the Python library scikit-learn which is a collection of libraries specifically made to make it simpler to implement machine learning, data mining, and data analysis algorithms.

Install Tensorflow

For this setup, we are going to install the open source machine learning framework Tensorflow. This is one of the most popular ML frameworks and has a lot of good documentation and examples online. To install it run the following conda command:

conda install -c conda-forge tensorflow

And now you should have the machine learning framework and all the python libraries needed to get started learning the concepts being machine learning! Of course, there are thousands of other libraries and frameworks out there but this should be a good start.

Printing Versions

The last thing for this setup is to write a simple Python script that prints out the versions of the main libraries we are using. This can be useful when trying to follow along with examples online as sometimes the syntax of library implementations will change over time.

In a new directory create a file versions.py and add the following Python code to it

## VERSIONS ##

# SciPy Environment
print('______ SciPy Environment ______')

# scipy
import scipy
print('scipy: %s' % scipy.__version__)

# numpy
import numpy
print('numpy: %s' % numpy.__version__)

# matplotlib
import matplotlib
print('matplotlib: %s' % matplotlib.__version__)

# pandas
import pandas
print('pandas: %s' % pandas.__version__)

# statsmodels
import statsmodels
print('statsmodels: %s' % statsmodels.__version__)

# scikit-learn
import sklearn
print('sklearn: %s' % sklearn.__version__)

# conda
import conda
print('conda: %s' % conda.__version__)

# Deep Learning Libraries
print('______ Deep Learning Libraries ______')

# tensorflow
import tensorflow
print('tensorflow: %s' % tensorflow.__version__)

# keras
import keras
print('keras: %s' % keras.__version__)

Running python versions.py will now print out the versions of the installed Python libraries!

Summary

In this article, we saw how to set up a simple machine learning environment for developing in Python by using Anaconda to install the needed libraries and frameworks.

Tyler Moon

Published a year ago

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