Top ML Frameworks

- Overview

- Top ML Frameworks

Machine learning (ML) frameworks are tools, libraries, and resources that provide a standardized way for developers and other professionals to tailor the ML algorithm’s functionality without comprising the underlying code. 

The frameworks provide a simpler user interface that is accessible to many professionals instead of making it necessary to know and read code. 

Most of these are Python ML frameworks, primarily because Python is the most popular ML programming language.

Popular ML Frameworks:

• TensorFlow: A versatile framework developed by Google, widely used for deep learning (DL) and neural networks.
• PyTorch: Another popular choice, especially in research and production, known for its dynamic computation graphs and ease of use.
• scikit-learn: A comprehensive library for classical machine learning algorithms, popular for its user-friendly API.
• Spark ML:A machine learning library for the Apache Spark platform, designed for large-scale data processing and distributed training.
• H2O.ai:A platform for scalable ML and AI, often used in enterprise settings.
• Keras: A high-level API that can be used on top of TensorFlow or other backends, making it a good choice for rapid prototyping and model development.
• XGBoost: An optimized library for gradient boosting algorithms, frequently used in machine learning competitions.