---
title: "Popular Libraries"
description: "Learn how to create, train, and evaluate machine learning models in the research environment in QuantConnect with supported libraries."
canonical: https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries
markdown: https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/04%20Research%20Environment/08%20Machine%20Learning/03%20Popular%20Libraries
modified: 2026-10-30
index: https://www.quantconnect.com/llms.txt
---

# Popular Libraries

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Research Environment](https://www.quantconnect.com/docs/v2/research-environment.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/research-environment/machine-learning/popular-libraries/aesera.md)
- [GPlearn](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/gplearn.md)
- [Hmmlearn](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/hmmlearn.md)
- [Keras](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/keras.md)
- [PyTorch](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/pytorch.md)
- [Scikit-Learn](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/scikit-learn.md)
- [Stable Baselines](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/stable-baselines.md)
- [TensorFlow](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/tensorflow.md)
- [Tslearn](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/tslearn.md)
- [XGBoost](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/xgboost.md)

## Pages in This Section

- [Aesera](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/aesera.md): Learn how to create, train, and evaluate machine learning models in the research environment in QuantConnect with Aesera library.
- [GPlearn](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/gplearn.md): Learn how to create, train, and evaluate machine learning models in the research environment in QuantConnect with Gplearn library.
- [Hmmlearn](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/hmmlearn.md): Learn how to create, train, and evaluate machine learning models in the research environment in QuantConnect with Hmmlearn library.
- [Keras](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/keras.md): Learn how to create, train, and evaluate machine learning models in the research environment in QuantConnect with Keras library.
- [PyTorch](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/pytorch.md): Learn how to create, train, and evaluate machine learning models in the research environment in QuantConnect with PyTorch library.
- [Scikit-Learn](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/scikit-learn.md): Learn how to create, train, and evaluate machine learning models in the research environment in QuantConnect with Scikit-Learn library.
- [Stable Baselines](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/stable-baselines.md): Learn how to create, train, and evaluate machine learning models in the research environment in QuantConnect with Stable Baselines library.
- [TensorFlow](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/tensorflow.md): Learn how to create, train, and evaluate machine learning models in the research environment in QuantConnect with Tensorflow library.
- [Tslearn](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/tslearn.md): Learn how to create, train, and evaluate machine learning models in the research environment in QuantConnect with Tslearn library.
- [XGBoost](https://www.quantconnect.com/docs/v2/research-environment/machine-learning/popular-libraries/xgboost.md): Learn how to create, train, and evaluate machine learning models in the research environment in QuantConnect with XGBoost library.