TensorFlow: As mentioned earlier, TensorFlow is an open-source library for dataflow and differentiable programming, widely used for building machine learning models.
Scikit-learn: This library is built on top of NumPy and SciPy and is designed for data mining and data analysis. It provides a wide range of tools for model fitting, including linear and logistic regression, support vector machines, decision trees, and more.
Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It is a user-friendly and easy to use library to quickly build deep learning models.
PyTorch: PyTorch is an open-source machine learning library based on the Torch library. It provides a seamless integration of computation graph with deep learning models and it is widely used in computer vision and natural language processing tasks.
XGBoost: XGBoost is a library for gradient boosting decision trees, which is an efficient and widely used method for fitting a variety of models, including linear and logistic regression, decision trees, and more.
LightGBM: LightGBM is a gradient boosting framework that uses tree-based learning algorithms. It is designed to be efficient and scalable, making it well-suited for large datasets.
CatBoost: CatBoost is an open-source gradient boosting library that is especially effective with categorical features. It is designed to handle missing values and categorical features, making it a good choice for datasets with a lot of categorical variables.
RandomForest: RandomForest is an open-source library for building random forest models, which are a type of ensemble model that can be used for both classification and regression tasks.
These are some of the most popular open-source libraries for building machine learning models, but there are many other options available as well, depending on the specific needs of your project.