Top 9 Python Libraries for Machine Learning in 2022

Machine learning and artificial intelligence libraries are available in almost all languages, but Python remains the most popular programming language. One of the most important aspects that make this language the first choice of developers and enthusiasts is its huge community and it has more than 137,000 data science libraries.

The community on GitHub contributes almost daily to make the library better and overcome problems and challenges in AI/ML.

Here’s a list of the best Python libraries, most contributed, and most used in 2022!

tensor flow

Developed by the Google Brain team in 2015, TensorFlow is the most popular open source library for building deep learning applications. This repository focuses on differential programming and neural networks, allowing beginners and professionals to design and architect using CPUs and GPUs.

TensorFlow has a machine learning ecosystem of tools, libraries, and a GitHub community with over 3,200 contributors and 169,000 stars.


Developed for fast testing of deep neural networks, Keras is an open-source library interface from TensorFlow. It allows developers to build models, analyze datasets and visualize graphs. It also runs on Theano and allows training neural networks with very little code. Due to its high scalability and flexibility, it is used by organizations such as NASA and YouTube.

With over 1,000 contributors and 56,000 stars, Keras releases new versions and improvements on GitHub almost every week.


NumPy or Numerical Python, also created in 2015, is one of the key libraries for mathematical and scientific computing. It is widely used by scientists to analyze data due to its ability to perform various mathematical operations such as linear algebra, Fourier transform, and matrix calculation functions. NumPy is also used to improve the performance of ML models without being too complex and using much less memory with multidimensional arrays.

With over 1,400 contributors and 22,000 stars, the GitHub community is actively making improvements. NumPy is also the basis for other libraries such as Matplotlib, SciPy, and Pandas.


Based on Torch (a programming language framework for C), PyTorch is an open source Python library for creating computational graphs that can be modified in real time. It is very popular among data scientists and machine learning enthusiasts developing NLP or computer vision based applications.

Developed by Meta AI, PyTorch is very similar to TensorFlow, with the processing power of NumPy. It has over 2,500 contributors and 60,000 stars.


Pandas is a flexible and powerful Python library for data analysis and manipulation, providing data structures for easier handling of relational, multidimensional, and labeled data. Data management is easier with this library as it provides series and data frames for precise data alignment and merging. Installation requires NumPy, dateutil, and pytz.

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


SciPy is another widely used machine learning library designed to be used with NumPy arrays for scientific and engineering computations on large datasets. It is used for data visualization and manipulation and is considered one of the best tools for scientific analysis. It is considered a more user-friendly repository than NumPy.

Besides Python, it is also popular in C and Fortran. The GitHub repository has over 1,200 contributors and 10,000 stars.


Matplotlib is a plotting library for Python, which basically means it’s used to create static, animated, and interactive visualizations. It aims to remove the need for the MATLAB statistical language and works as a unit of NumPy and SciPy. The library can create publication-quality diagrams and draw them using an object-oriented API relying on a Python GUI.

Matplotlib’s GitHub repository has more than 1,200 contributors and 16,500 stars.


Scikit-learn is built on top of SciPy, NumPy, and Matplotlib, and provides gradient boosting, vector machine support, and random forests for regression, classification, and clustering. It is used in data mining and traditional ML applications. Key features include deriving information from image and text data, and incorporating predictions from supervised models using ensemble methods.

This GitHub repository for machine learning has over 52,000 stars and 2,500 contributors.


XGBoost is a distributed gradient boosting library optimized to build ML algorithms that accurately and quickly solve various data science problems using its parallel tree boosting algorithm. The library is also available for R, Julia, C++, Java, and Scala, as well as Python.

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

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