Python handles machine learning (ML) libraries through its robust ecosystem, offering a wide range of tools and frameworks that simplify the implementation of ML algorithms. Popular libraries include TensorFlow, Keras, PyTorch, scikit-learn, and pandas, each serving different purposes within the ML pipeline. These libraries are designed to work seamlessly with Python, leveraging its simplicity and readability to make complex ML tasks more approachable.

TensorFlow and PyTorch are deep learning frameworks that provide tools for building and training neural networks. TensorFlow, developed by Google, offers high-level APIs like Keras for rapid prototyping, while PyTorch, developed by Facebook, is known for its dynamic computational graph, which makes it easier to modify the network architecture during runtime.

Scikit-learn is widely used for classical machine learning tasks such as classification, regression, clustering, and dimensionality reduction. It provides simple and efficient tools for data analysis and modeling. Pandas is essential for data manipulation and analysis, offering data structures like DataFrames to handle structured data easily.

The interoperability of these libraries allows for the seamless integration of various ML tasks, from data preprocessing and model building to evaluation and deployment. For those new to this field, starting with a Python course for beginners can provide a strong foundation for utilizing these powerful libraries effectively.