[**Machine Learning**](https://en.wikipedia.org/wiki/Machine_learning) (ML) is a subset of artificial intelligence where systems learn patterns from data to make predictions or decisions without being explicitly programmed. It includes supervised learning (e.g. classification, regression), unsupervised learning (e.g. clustering, dimensionality reduction), and reinforcement learning (e.g. game agents, robotics). Core tools/libraries: - [**scikit-learn**](https://scikit-learn.org/) – classical ML algorithms in Python - [**TensorFlow**](https://www.tensorflow.org/) / [**PyTorch**](https://pytorch.org/) – deep learning frameworks - [**XGBoost**](https://xgboost.ai/) / [**LightGBM**](https://github.com/microsoft/LightGBM) – gradient boosting for structured data - [**AutoML**](https://en.wikipedia.org/wiki/Automated_machine_learning) tools like [AutoGluon](https://github.com/autogluon/autogluon) simplify end-to-end modeling