Machine learning (ML) is a field of artificial intelligence (AI) that involves the development of algorithms and models that can learn from data without being explicitly programmed. ML algorithms are designed to automatically improve their performance over time as they are exposed to more data.
There are several different types of machine learning, including:
- Supervised learning: This involves training an ML model on a labeled dataset, where the correct output is provided for each example in the training set. The model is then able to make predictions on new, unseen data based on what it has learned from the training set.
- Unsupervised learning: This involves training an ML model on an unlabeled dataset, where the model is not provided with the correct output for each example. The model must discover the underlying structure in the data and find patterns and relationships on its own.
- Semi-supervised learning: This is a combination of supervised and unsupervised learning, where the model is trained on a dataset that is partially labeled and partially unlabeled.
- Reinforcement learning: This involves training an ML model to make a series of decisions in an environment in order to maximize a reward. The model learns through trial and error, improving its decision-making over time as it receives feedback on the consequences of its actions.
Machine learning has a wide range of applications, including image and speech recognition, natural language processing, and predictive modeling. It is being used in many different industries to improve decision-making, automate processes, and solve complex problems.
