7 Useful Python Machine Learning Libraries You Should Use in Your Next Project

Original Source Here

7 Useful Python Machine Learning Libraries You Should Use in Your Next Project

Photo by Alex Chumak on Unsplash

There is a proverb “You don’t have to reinvent the wheel”. Libraries are the best example of that. It helps you to write complex and time-consuming functionality in an easy way. According to me, a good project uses some of the best libraries available

Machine learning is one of the most demanding and popular topics in the current world. Python is the most used programming language for Machine Learning. Here I have compiled 7 useful Python libraries that will help you in your development journey.


This one is from Microsoft. This is a unified deep learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix operations upon their inputs. It allows users to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). This library has more than 17k stars on GitHub.

2. einops

This library provides flexible and powerful tensor operations for readable and reliable code. It supports NumPy, PyTorch, TensorFlow, Jax, and others. It supports features like stacking, reshaping, transposition, squeeze/unsqueeze, repeat, tile, concatenate, view, numerous reductions, etc. It has more than 5.5k stars on GitHub.

3. xlearn

This is a high-performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM), all of which can be used to solve large-scale machine learning problems. It is especially useful for solving machine learning problems on large-scale sparse data. This library has more than 3k stars on GitHub.

4. skorch

The goal of this library is to make it possible to use PyTorch with sklearn. This is achieved by providing a wrapper around PyTorch that has a learn interface. This library does not re-invent the wheel, instead getting as much out of your way as possible. If you are familiar with sklearn and PyTorch, you don’t have to learn any new concepts, and the syntax should be well known. It has more than 4k stars on GitHub.

5. stellargraph

This library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data. It includes features like representation learning for nodes and edges, to be used for visualization and various downstream machine learning tasks, classification of whole graphs, link prediction, and many more. It has more than 2.5k stars on GitHub.

6. modAL

This is an active learning library for Python3, designed with modularity, flexibility, and extensibility in mind. Built on top of scikit-learn, it allows you to rapidly create active learning workflows with nearly complete freedom. What is more, you can easily replace parts with your custom-built solutions, allowing you to design novel algorithms with ease. It has more than 1.5k stars on GitHub.

7. bentoml

This library makes it easy to create ML-powered prediction services that are ready to deploy and scale. It includes features like building scalable and high-performance prediction services, continuously deploying, monitoring, and operating prediction services in production, accelerating and standardizing the process of taking ML models to production, etc. It has more than 4k stars on GitHub.

That’s all for today. I believe these libraries will help you a lot in your development journey.

If you know of any other beautiful Machine Learning libraries please share them in the comments. Until we meet again. Cheers!


Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot

%d bloggers like this: