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 for machine learning that were the most contributed and used!
1. 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.
TensorFlow hosts an ecosystem for machine learning with tools, libraries, and a GitHub community with more than 3,200 contributors and 169,000 stars.
Source: Tensorflow
2. 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.
Source: Keras
3. 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.
Source: NumPy
4. 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.
Source: PyTorch
5. 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.
Source: Pandas
6. 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.
Source: SciPy
7. 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.
Source: Matplotlib
8. 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.
Source: Scikit-Learn
9. 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.
Source: XGBoost
10. LightGBM
LightGBM (Light Gradient Boosting Machine) is a highly efficient and scalable framework for gradient boosting, designed to optimise both speed and performance in machine learning tasks. Developed by Microsoft, it is widely used for tasks such as classification, regression, and ranking, especially when working with large datasets and high-dimensional data.
LightGBM's key strength lies in its ability to handle large-scale datasets with faster training times and lower memory consumption compared to traditional gradient boosting algorithms. It achieves this through techniques like histogram-based decision tree learning and leaf-wise tree growth, which improve the efficiency of the model.
Source: LightGBM
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