Machine Learning (ML) is a subset of artificial intelligence that enables systems to automatically learn and improve from experience without requiring explicit programming. It involves the use of algorithms that iteratively learn patterns from data, make predictions, or take actions. Common types include:

• Supervised Learning: Algorithms are trained on labeled data, where the model learns to map inputs to known outputs (e.g., classification, regression).
• Unsupervised Learning: Algorithms identify hidden patterns or intrinsic structures in unlabeled data (e.g., clustering, dimensionality reduction).
• Reinforcement Learning: Models learn by interacting with an environment to maximize long-term rewards through trial and error.

Applications range from image recognition and language translation to recommendation systems and autonomous vehicles. Key tools and frameworks in ML include Python libraries such as Scikit-learn, TensorFlow, and PyTorch.