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(Created page with "{{see also|Machine learning terms}} ==Introduction== Machine learning is a branch of artificial intelligence that seeks to develop algorithms and statistical models that enable computers to perform tasks without explicit programming. Training plays an integral role in this process, as it enables the algorithm to learn from data and make predictions based on patterns it detects. In this article, we'll examine training in machine learning in depth - its purpose, different...") |
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Machine learning employs various types of training, such as supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. | Machine learning employs various types of training, such as supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. | ||
===Supervised Learning== | ===Supervised Learning=== | ||
Supervised learning is the most common type of machine learning training. This method involves providing the algorithm with a labeled dataset, in which each data point has an assigned class label. The algorithm then utilizes this labeled data to learn relationships between features in the data and their corresponding labels. Ultimately, supervised learning aims to build a model capable of accurately predicting labels for new, unseen data sets. | Supervised learning is the most common type of machine learning training. This method involves providing the algorithm with a labeled dataset, in which each data point has an assigned class label. The algorithm then utilizes this labeled data to learn relationships between features in the data and their corresponding labels. Ultimately, supervised learning aims to build a model capable of accurately predicting labels for new, unseen data sets. | ||
===Unsupervised Learning== | ===Unsupervised Learning=== | ||
Unsupervised learning is a type of machine-learning training in which an algorithm is given data that has not yet been labeled. The objective is for this type of algorithm to discover patterns and relationships without the assistance of labeled information. Unsupervised learning is often employed for clustering and dimensionality reduction tasks, as it allows the computer to recognize structure within data that may otherwise go undetected. | Unsupervised learning is a type of machine-learning training in which an algorithm is given data that has not yet been labeled. The objective is for this type of algorithm to discover patterns and relationships without the assistance of labeled information. Unsupervised learning is often employed for clustering and dimensionality reduction tasks, as it allows the computer to recognize structure within data that may otherwise go undetected. | ||
===Semi-Supervised Learning== | ===Semi-Supervised Learning=== | ||
Semi-supervised learning is a type of training that incorporates elements from both supervised and unsupervised learning methods. In this type of scenario, an algorithm receives some labeled data but most of it remains unlabeled. The algorithm then utilizes this labeled information to direct its learning process and make predictions about unlabeled data. | Semi-supervised learning is a type of training that incorporates elements from both supervised and unsupervised learning methods. In this type of scenario, an algorithm receives some labeled data but most of it remains unlabeled. The algorithm then utilizes this labeled information to direct its learning process and make predictions about unlabeled data. | ||
===Reinforcement Learning== | ===Reinforcement Learning=== | ||
Reinforcement learning is a type of training in which an algorithm learns from its own actions and the outcomes. In reinforcement learning, an algorithm is given an environment and set of actions it can take within that environment; then it learns which actions produce the most favorable outcomes based on feedback from its environment. | Reinforcement learning is a type of training in which an algorithm learns from its own actions and the outcomes. In reinforcement learning, an algorithm is given an environment and set of actions it can take within that environment; then it learns which actions produce the most favorable outcomes based on feedback from its environment. | ||