Machine Learning Libraries in Python

Python is one of the best programming languages out there, with an extensive coverage in scientific computing: computer vision, artificial intelligence, mathematics, astronomy to name a few. Unsurprisingly, this holds true for machine learning as well.

In my opinion, Python is one of the best languages you can use to learn (and implement) machine learning techniques for a few reasons:

• It's simple: Python is now becoming the language of choice among new programmers thanks to its simple syntax and huge community.
• It's powerful: Just because something is simple doesn't mean it isn't capable. Python is also one of the most popular languages among data scientists and web programmers. Its community has created libraries to do just about anything you want, including machine learning.
• Lots of ML libraries: There are tons of machine learning libraries already written for Python. You can choose one of the hundreds of libraries based on your use-case, skill, and need for customization. (1)

Some of my favorite Machine Learning Libraries in Python are:

• Scikit-learn: comprehensive and easy to use.
• PyBrain: Neural networks are one thing that are missing from SciKit-learn, but this module makes up for it.
• nltk: really useful if you’re doing anything NLP or text mining related.
• Theano: efficient computation of mathematical expressions using GPU. Excellent for deep learning.
• Pylearn2: machine learning toolbox built on top of Theano - in very early stages of development.
• MDP (Modular toolkit for Data Processing): a framework that is useful when setting up workflows.

To read more about Machine learning libraries in Python and how to use them, please visit webpage (1) and (2).

Here is Matt Spitz's presentation on practical machine learning in Python, which is really interesting: