What is machine learning?

Machine learning is defined as a subset of artificial intelligence that enables systems to learn from data and improve their performance over time without being explicitly programmed for each specific task. This process involves using algorithms to analyze and interpret large amounts of data, allowing the system to identify patterns or trends that can inform decision-making or predictions.

In this context, machine learning algorithms can range from supervised learning, where the model is trained on labeled data, to unsupervised learning, where the model finds hidden patterns in data without labels. As the system encounters more data over time, it refines its understanding and improves its accuracy in making predictions or classifying information. This concept is essential in various applications such as image recognition, natural language processing, and recommendation systems, where adapting to new data is crucial for success.

Other options focus on different aspects of computing. For example, manually coding software applications does not involve the adaptive learning capabilities of machine learning, while optimizing programming languages and creating databases are not related to the learning-based approach of machine learning. Thus, identifying machine learning as a subset of artificial intelligence emphasizes its unique capability to learn from data, setting it apart from other methodologies in computer science.