Advertisement

Top 9 Python Libraries for Machine Learning in 2022

From data visualisation to deep learning libraries, Python is the most valuable language for machine learning.
Listen to this story

Machine learning and artificial intelligence libraries are available in almost all the languages but Python remains the most popular programming language of all. One of the most important aspects that makes the language the go-to choice for developers and enthusiasts is its sizeable community and the fact that it has more than 137,000 libraries for data science. 

The communities on GitHub are contributing almost everyday to make the libraries even better and overcome the existing issues and challenges in AI/ML.

Here’s a list of the top Python libraries that were the most contributed to and used in 2022!

THE BELAMY

Sign up for your weekly dose of what's up in emerging technology.

TensorFlow

Built by the Google Brain team in 2015, TensorFlow is the most famous open-source library for building deep learning applications. Specialising in differential programming and neural networks, the repository enables beginners and professionals to construct and architect using CPUs and GPUs.


Download our Mobile App



TensorFlow hosts an ecosystem for machine learning with tools, libraries, and a GitHub community with more than 3,200 contributors and 169,000 stars. 

Keras

Built for rapid testing of deep neural networks, Keras is an open-source library interface of TensorFlow. It enables developers in constructing models, analysing datasets, and visualisation of graphs. It also runs on top of ‘Theano’, enabling training of neural networks with very little code. Being highly scalable and flexible, it is used by organisations like NASA and YouTube, among several others. 

Keras has more than 1,000 contributors and 56,000 stars with new releases and improvements nearly every week on GitHub.

NumPy

Also created in 2015, NumPy or Numerical Python, is one of the key libraries for mathematical and scientific computing. Owing to its ability to perform various mathematical operations like linear algebra, fourier transform, and matrix calculation functions, it is widely used by scientists to analyse data. NumPy is also used for increasing the performance of ML models without much complexity and requiring a lot less storage with multidimensional arrays.

With more than 1,400 contributors and 22,000 stars, the GitHub community is actively making improvements. NumPy is also the foundation for other libraries like Matplotlib, SciPy, and Pandas.

PyTorch

Based on Torch, a programming language framework on C, PyTorch is an open source Python library for creating computational graphs that are changeable in real-time. It is very popular for data scientists and machine learning enthusiasts who are building NLP or computer vision-based applications. 

PyTorch was developed by Meta AI, and is very similar to TensorFlow and has computational power like NumPy. It hosts more than 2,500 contributors and 60,000 stars.

Pandas

A flexible and powerful Python library for data analysis and manipulation, Pandas provides data structures for easier working with relational, multidimensional, and labelled data. Managing data using this library is easier as it provides Series and DataFrames for concise data alignment and merging. The installation requires NumPy, dateutil, and pytz.

The GitHub repository is an active community with more than 36,000 stars and 2,700+ contributors with updates every few days.

SciPy

Another actively used machine learning library built to work on NumPy arrays, SciPy is used for scientific and technical computing for large sets of data. It is used for data visualisation and manipulation and is regarded as one of the best for scientific analysis. It is considered as a more user-friendly repository than NumPy.

Along with Python, it is also very popular in C and Fortran. The GitHub repository has more than 1,200 contributors and 10,000 stars.

Matplotlib

Matplotlib is a plotting library for Python, which essentially means that it is used for creating static, animated, and interactive visualisations. It was developed to remove the need for MATLAB statistical language and works like a unity of NumPy and SciPy. The library can create publication-quality plots and relies on Python GUI for plotting them with object-oriented APIs.

The GitHub repository for Matplotlib has more than 1,200 contributors and 16,500 stars. 

Scikit-Learn

Built on top of SciPy, NumPy, and Matplotlib, Scikit-learn has gradient boosting, support for vector machines, and random forests for regression, classification, and clustering. It is used for data mining and conventional ML applications. Its main features include inferring information from picture and text data and merging prediction of supervised models using ensemble approaches.

This machine learning repository of GitHub has more than 52,000 stars and 2,500 contributors.

XGBoost

A distributed gradient boosting library, XGBoost is optimised to create ML algorithms using its parallel tree boosting algorithm for addressing various data science issues accurately and quickly. The library, along with Python, is also available on R, Julia, C++, Java, and Scala.

XGBoost has around 500+ contributors and more than 23,000 stars on GitHub.

Read: 15 Most Popular R Libraries You Need To Know in 2022

More Great AIM Stories

Mohit Pandey
Mohit is a technology journalist who dives deep into the Artificial Intelligence and Machine Learning world to bring out information in simple and explainable words for the readers. He also holds a keen interest in photography, filmmaking, and the gaming industry.

AIM Upcoming Events

Regular Passes expire on 3rd Mar

Conference, in-person (Bangalore)
Rising 2023 | Women in Tech Conference
16-17th Mar, 2023

Early Bird Passes expire on 17th Feb

Conference, in-person (Bangalore)
Data Engineering Summit (DES) 2023
27-28th Apr, 2023

Conference, Virtual
Deep Learning DevCon 2023
27 May, 2023

3 Ways to Join our Community

Telegram group

Discover special offers, top stories, upcoming events, and more.

Discord Server

Stay Connected with a larger ecosystem of data science and ML Professionals

Subscribe to our Daily newsletter

Get our daily awesome stories & videos in your inbox
AIM TOP STORIES

A beginner’s guide to image processing using NumPy

Since images can also be considered as made up of arrays, we can use NumPy for performing different image processing tasks as well from scratch. In this article, we will learn about the image processing tasks that can be performed only using NumPy.

RIP Google Stadia: What went wrong?

Google has “deprioritised” the Stadia game streaming platform and wants to offer its Stadia technology to select partners in a new service called “Google Stream”.