Machine Learning

Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on using data and algorithms to enable AI to imitate the way that humans learn and think.

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance.

ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. When applied to business problems, it is known under the name predictive analytics.

Although not all machine learning is statistically based, computational statistics is an important source of the field's methods.

(From Wikipedia)

Machine learning is the dominant subset of artificial intelligence. It underlies generative AI systems like ChatGPT and DALL-E 2.

There are three components to machine learning: an algorithm or a set of algorithms, training data and a model. An algorithm is a set of procedures. In machine learning, an algorithm learns to identify patterns after being trained on a large set of examples – the training data. Once a machine-learning algorithm has been trained, the result is a machine-learning model. The model is what people use.

For example, a machine-learning algorithm could be designed to identify patterns in images, and training data could be images of dogs. The resulting machine-learning model would be a dog spotter. You would feed it an image as input and get as output whether and where in the image a set of pixels represents a dog.