---
title: "Popular Libraries"
description: "These are examples of using some of the most common machine learning libraries in an algorithm."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/31%20Machine%20Learning/03%20Popular%20Libraries
modified: 2026-10-31
index: https://www.quantconnect.com/llms.txt
---

# Popular Libraries

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Writing Algorithms](https://www.quantconnect.com/docs/v2/writing-algorithms.md) > Machine Learning > Popular Libraries

> Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page.

These are examples of using some of the most common machine learning libraries in an algorithm. Click one to learn more.

- [Aesera](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/aesera.md)
- [Copula](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/copula.md)
- [GPlearn](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/gplearn.md)
- [Hmmlearn](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/hmmlearn.md)
- [Keras](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/keras.md)
- [PyTorch](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/pytorch.md)
- [Scikit-Learn](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/scikit-learn.md)
- [Tensorflow](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/tensorflow.md)
- [Tslearn](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/tslearn.md)
- [XGBoost](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/xgboost.md)

## Pages in This Section

- [Aesera](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/aesera.md): This page explains how to build, train, deploy and store Aesera models.
- [Copula](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/copula.md): This page explains how to build, train, deploy and store Copula models.
- [GPlearn](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/gplearn.md): This page explains how to build, train, deploy and store GPlearn models.
- [Hmmlearn](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/hmmlearn.md): This page explains how to build, train, deploy and store Hmmlearn models.
- [Keras](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/keras.md): This page explains how to build, train, deploy and store Keras models.
- [PyTorch](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/pytorch.md): This page explains how to build, train, deploy and store PyTorch models.
- [Scikit-Learn](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/scikit-learn.md): This page explains how to build, train, deploy and store Scikit-Learn models.
- [Tensorflow](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/tensorflow.md): This page explains how to build, train, deploy and store Tensorflow v1 models. To view the tutorial on Tensorflow 2, see Keras.
- [Tslearn](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/tslearn.md): This page explains how to build, train, deploy and store Tslearn models.
- [XGBoost](https://www.quantconnect.com/docs/v2/writing-algorithms/machine-learning/popular-libraries/xgboost.md): This page explains how to build, train, deploy and store XGBoost models.