Machine Learning Frameworks are software libraries and platforms that provide the foundational tools, algorithms, and infrastructure needed to develop, train, and deploy machine learning models efficiently.

Popular ML frameworks include:

• TensorFlow: Google's open-source platform for deep learning and neural networks
• PyTorch: Facebook's dynamic neural network framework popular in research
• Scikit-learn: Python library for traditional machine learning algorithms
• Keras: High-level neural networks API (now integrated with TensorFlow)
• XGBoost: Optimized gradient boosting framework
• Apache Spark MLlib: Scalable machine learning library

These frameworks provide pre-built algorithms, data processing utilities, model training capabilities, and deployment tools, enabling developers to focus on solving problems rather than implementing algorithms from scratch.