Machine Learning is a field of artificial intelligence focused on developing algorithms that allow computers to learn patterns and make predictions or decisions based on data. Rather than being explicitly programmed with if-else rules, ML models improve their performance P at tasks T through experience E (as a classic definition goes). There are several categories: supervised learning (learning from labeled examples, including regression and classification), unsupervised learning (finding structure in unlabeled data, e.g., clustering, dimensionality reduction), semi-supervised learning (mix of labeled and unlabeled data), reinforcement learning (learning to take actions to maximize reward), etc. Popular algorithms range from linear models, decision trees, and SVMs to more complex ones like neural networks and ensemble methods. ML is behind many applications: recommendation systems, speech recognition, image recognition, etc. Key aspects of ML include generalization (performing well on unseen data), dealing with overfitting/underfitting, feature engineering, and evaluation methodology.