Finally, the last library we are going to see for now that will help us with our machine learning programs is going to be scikit-learn. This is probably one of the most useful libraries for Machine Learning in Python. It is an open-source library and it brings us a range of supervised and unsupervised learning algorithms.

This library includes the next libraries or packages:

• NumPy: N-dimensional matrix library.
• pandas: Data structures and analysis.
• SciPy: Essential library for computer science.
• Matplotlib: 2D data representation.
• IP[y]: Improved interactive console.
• SymPy: Symbolic mathematics.

Considering the extension and the fact that includes some of the libraries we have already explored, this article is going to be very short and without code examples. Basically, we are going to see a shortlist of basic functions or benefits the library offers us like:

• Supervised learning algorithms: It brings us a variety of supervised algorithms.
• Cross-validation: The library brings us instructions to implement some of the model’s precision verification methods.
• Unsupervised learning algorithms: It brings us a variety of unsupervised algorithms.
• Data sets: A miscellaneous collection of data sets.
• Characteristic extraction and selection: It is very useful to extract characteristics from images and texts. In addition, it can help us to identify significant attributes.
• Community: It has some community behind improving the library.

That’s all. Quick and simple. Let’s save some energy for the next article where we are going to start digging a little bit on Machine Learning theory.