AI applications rely heavily on machine learning (ML) algorithms to derive insights from Big Data. The development and deployment of these algorithms are facilitated by specialized libraries and frameworks.

TensorFlow, developed by Google, is one of the most popular open-source frameworks for deep learning. It supports a wide range of tasks, from simple linear models to complex neural networks for image recognition, natural language processing (NLP), and reinforcement learning. TensorFlow's flexibility allows it to run on various platforms, including CPUs, GPUs, and even mobile devices.

PyTorch, developed by Facebook's AI Research lab, has gained popularity due to its dynamic computation graph, which offers greater flexibility during model development. PyTorch is particularly favored in academic research and for prototyping deep learning models. Its integration with Python makes it accessible to data scientists familiar with Python's ecosystem.

For traditional machine learning algorithms, Scikit-learn is a widely used library in the Python ecosystem. It provides simple and efficient tools for data mining and data analysis, supporting algorithms for classification, regression, clustering, and dimensionality reduction. XGBoost and LightGBM are specialized libraries designed for gradient boosting, offering high performance and efficiency in predictive analytics tasks, especially in structured data competitions like those hosted on Kaggle.

Additionally, Keras, a high-level API built on top of TensorFlow, simplifies the creation of neural networks with an easy-to-use interface. It allows for quick experimentation, making it suitable for beginners and professionals alike.