Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables systems to learn from data and make predictions or decisions without explicit programming. It involves developing algorithms and statistical models to identify patterns and insights from data. ML is widely applied in fields like automation, predictive analytics, and personalization.

Types of Machine Learning

ML is categorized into several types based on the nature of the data and learning process:

• Supervised Learning: Trains models on labeled data to predict outcomes or classify data. Examples include Linear Regression, Logistic Regression, and Decision Trees.

• Unsupervised Learning: Identifies patterns in unlabeled data, such as clustering (e.g., K-Means) and dimensionality reduction (e.g., PCA).

• Reinforcement Learning: Learns through trial and error by interacting with an environment to maximize rewards. Examples include Q-Learning and SARSA.

• Semi-Supervised Learning: Combines a small amount of labeled data with a large amount of unlabeled data, useful when labeling is expensive.

• Self-Supervised Learning: Generates its own labels from data, often used in training large-scale models like transformers.