Skip to content

Machine Learning

  • Systems learn from data using algorithms to recognize patterns and improve task performance over time.
  • Training typically uses large datasets (often labeled) to build models that make predictions or take actions on new data.
  • Common applications include image recognition and natural language processing (NLP).

Machine learning is a type of artificial intelligence that allows systems to improve their performance on a specific task by learning from data, without being explicitly programmed.

Machine learning relies on algorithms that process large amounts of data to recognize patterns. Those patterns are used to make predictions or take actions based on new inputs. By training on data (often labeled examples), a machine learning model extracts characteristics of the task and applies that knowledge to unseen data, allowing the system to improve performance over time without explicit programming for each decision.

A system is trained to identify objects in images (for example, cars, buildings, or animals) by being fed a large dataset of labeled images, where each image has been manually labeled with the objects it contains. The machine learning algorithm uses this data to learn the characteristics of each object and develop a model that can identify them in new images.

A system is trained to understand and generate human language (such as for chatbots or virtual assistants) by being fed a large dataset of human language data, such as transcripts of conversations or written documents. The machine learning algorithm uses this data to learn the structure and meaning of human language and develop a model that can understand and generate it.

In both examples, the machine learning algorithm uses labeled data to learn the characteristics of the task and then uses that knowledge to make predictions or take actions on new data, enabling improved performance over time.

  • Artificial intelligence
  • Algorithms
  • Image recognition
  • Natural language processing (NLP)