Scikit-learn, PyTorch, and TensorFlow remain core tools for structured data and deep learning tasks.New libraries like JAX, ...

Snowpark for Python gives data scientists a nice way to do DataFrame-style programming against the Snowflake data warehouse, including the ability to set up full-blown machine learning pipelines ...

What is this book about? Machine Learning with LightGBM and Python is a comprehensive guide to learning the basics of machine learning and progressing to building scalable machine learning systems ...

This project is focused on the Deployment phase of machine learning. The Docker and FastAPI are used to deploy a dockerized server of trained machine learning pipeline. Attendance prediction tool for ...

Learn about some of the best Python libraries for programming Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL).

Explore the top AI tools and essential skills every data engineer needs in 2025 to stay ahead—covering data pipelines, ML ...

Each machine learning pipeline will be slightly different depending on the model's use case and the organization using it. However, since the pipeline frequently adheres to a typical machine learning ...

Discover five powerful Python libraries that enable data scientists to interpret and explain machine learning models effectively.

The popular PyTorch Python project for data scientists and machine learning developers has become the latest open source project to be targeted with a dependency confusion attack.

Python has a plethora of machine learning libraries, but the top 5 libraries are TensorFlow, Keras, PyTorch, Scikit-learn, and Pandas. These libraries offer a wide range of tools for various ...