Programming libraries refer to the sets of code files that have been created to develop software in a simple way . Thanks to them, developers can avoid code duplication and minimize errors with greater agility and lower cost. There are many bookstores, focused on different activities. A few weeks ago we saw some examples of libraries for creating visualizations , and this time we are going to focus on useful libraries for machine learning tasks .

These libraries are highly practical when implementing Machine Learning flows . This discipline, belonging to the field of Artificial Intelligence, uses algorithms that offer, for example, the ability to identify patterns in massive data or the ability to help develop predictive analysis.

Below, we show you some of the most popular data analysis and Machine Learning libraries that currently exist for the main programming languages, such as Python or R:

Libraries for Python

NumPy

• Description:

This Python library is specialized in mathematical computation and big data analysis . It allows working with arrays that allow representing collections of data of the same type in various dimensions, as well as very efficient functions for their manipulation.

• Support materials:

Here we find the Beginner's Guide , with basic concepts and tutorials, the User's Guide , with information on general features, or the Contributor's Guide , to help maintain and develop the code or write technical documentation. NumPy also has a Reference Guide that details functions, modules and objects included in this library, as well as a series of tutorials to learn how to use it easily.

Pandas

• Description :

It is one of the most used libraries for data processing in Python . This data analysis and manipulation tool is characterized, among other aspects, by defining new data functionalities based on the arrays of the NumPy library . It allows you to easily read and write files in CSV, Excel format and specify queries to SQL databases .

• Support materials:

Its website has different documents such as the User's Guide , with detailed basic information and useful explanations, the Developer's Guide , which details the steps to follow when identifying errors or suggestions for improvements in functionalities, as well as the Reference Guide , with a detailed description of its API. In addition, it offers a series of tutorials contributed by the community and references on equivalent operations in other software and languages ​​such as SAS, SQL or R.

Scikit-learn

• Description:

Scikit-Learn is a library that implements a large number of Machine Learning algorithms for classification, regression, clustering , and dimensionality reduction tasks . In addition, it is compatible with other Python libraries such as NumPy, SciPy and Matplotlib (Matpotlib is a data visualization library and as such is included in the previous article ).

• Support materials:

This library has different help documents such as an Installation Manual , a User's Guide or a Glossary of common terms and elements of its API . In addition, it offers a section with different examples that illustrate the features of the library, as well as other sections of interest with tutorials , frequently asked questions or access to its GitHub .

Scipy

• Description:

This library features a collection of mathematical algorithms and functions built on top of the NumPy extension . It includes extension modules for Python on statistics, optimization, integration, linear algebra or image processing, among others.

• Support materials:

Like the previous examples, this library also has materials such as Installation Guides , User Guides , Developer Guides or a document with detailed descriptions of its API . It also provides information on act , a tool for running GitHub actions locally.

Libraries for R

mlr

• Description:

This library offers essential components to develop machine learning tasks, among others, preprocessing, pipelining , feature selection, visualization and implementation of supervised and unsupervised techniques using a wide range of algorithms.

• Support materials:

On its website, it has multiple resources for users and developers, among which a reference tutorial stands out that presents an extensive tour that covers the basic aspects of tasks, predictions or data preprocessing to the implementation of complex projects using advanced functions.

In addition, it has a section that redirects to GitHub in which it offers talks, videos and workshops of interest on the operation and uses of this library.

Tidyverse

• Description:

This library offers a collection of R packages designed for data science that provide very useful functionality to import, transform, visualize, model and communicate information from data. They all share the same design philosophy, grammar, and underlying data structures. The main packages that make it up are: dplyr, ggplot2, forcats, tibble, readr, stringr, tidyr and purrr.

• Support materials:

Tidyverse has a blog where you can find posts about programming, packages or tricks and techniques to work with this library. In addition, it has a section that recommends books and workshops to learn how to use this library in a simpler and more enjoyable way.

Caret

• Description:

This popular library contains an interface that unifies hundreds of functions for training classifiers and regressors under a single framework , greatly facilitating all stages of preprocessing, training, optimization and validation of predictive models. 

• Support materials:

The project website contains exhaustive information that makes it easier for the user to tackle the aforementioned tasks. References can also be found on CRAN and the project is hosted on GitHub . Some resources of interest for managing this library can be found through books such as Applied Predictive Modeling , articles , seminars or tutorials , among others.

Libraries to tackle Big Data tasks

TensorFlow

• Description:

In addition to Python and R, this library is also compatible with other languages ​​such as JavaScript, C++ or Julia. TensorFlow offers the ability to build and train ML models using APIs . The most prominent API is Keras , which allows building and training deep learning models (Deep Learning).

• Support materials:

On its website you can find resources such as previously established and developed models and data sets, tools , libraries and extensions , certification programs , knowledge about machine learning or resources and tools to integrate responsible AI practices . You can access their GitHub page here .

Dmlc XGBoost

• Description:

Scalable, portable and distributed "Gradient Boosting" (GBM, GBRT, GBDT) library supports C ++, Python, R, Java, Scala, Perl and Julia programming languages . This library allows you to solve many data science problems quickly and accurately and can be integrated with Flink, Spark and other cloud data flow systems to tackle Big Data tasks.

• Support materials:

On its website it has a blog with related topics such as algorithm updates or integrations, as well as a documentation section that has installation guides, tutorials, frequently asked questions, a user forum or packages for the different programming languages. You can access their GitHub page via this link .

H20

• Description:

This library combines the main algorithms of Machine Learning and statistical learning with Big Data , as well as being able to work with millions of records. H20 is written in Java , and follows the Key/Value paradigm to store data and Map/Reduce to implement algorithms. Thanks to its API , it can be accessed from R, Python or Scala.

• Support materials:

It has a series of videos in the form of a tutorial to teach and facilitate its use for users. On its GitHub page you can find additional resources such as blogs , projects, resources, research papers, courses or books about H20 .

In this article we have offered a sample of some of the most popular libraries that offer versatile functionality to tackle typical data science and machine learning tasks, although there are many others . This type of library is constantly evolving thanks to the possibility it offers its users to participate in its improvement through actions such as contributing to code writing, generating new documentation or reporting errors. All this allows you to continuously enrich and refine your results.

If you know of any other bookstore of interest that you want to recommend, you can leave us a message in comments or send us an email to dinamizacion@datos.gob.es

Content prepared by the datos.gob.es team.