Machine Learning (ML) is a branch of artificial intelligence (AI) that focuses on developing algorithms and statistical models that enable computers to learn from and make decisions based on data. Unlike traditional programming, where specific instructions are provided for every task, machine learning systems improve their performance by identifying patterns and insights from the data they process, without explicit programming for every possible scenario.

Key components of machine learning include:

1. Data: The foundational element for ML models, encompassing historical records, real-time information, or simulated datasets.

2. Algorithms: Mathematical procedures that process and learn from data to make predictions or decisions. Examples include linear regression, decision trees, and neural networks.

3. Training: The process of feeding data into an ML model to help it learn and adapt. During training, the model adjusts its parameters to minimize errors in predictions.

4. Evaluation: Assessing the model’s performance using a separate set of data to ensure it accurately interprets and predicts outcomes.

5. Deployment: Implementing the trained model in real-world applications to perform tasks such as image recognition, natural language processing, or recommendation systems.

Machine learning is widely used across various industries, including healthcare, finance, retail, and technology, to enhance decision-making, automate processes, and provide personalized experiences.