Machine Learning: Difference between revisions
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* '''Deep learning''': This type of machine learning is a subset of neural networks which uses a multi-layer architecture and can be used for [[image]] and speech recognition, natural language processing, and other tasks. | * '''Deep learning''': This type of machine learning is a subset of neural networks which uses a multi-layer architecture and can be used for [[image]] and speech recognition, natural language processing, and other tasks. | ||
Machine learning can be used to solve a wide range of problems, from simple tasks such as image classification, to more complex tasks such as natural language understanding, and autonomous systems. It can be applied in various industries such as healthcare, finance, manufacturing, transportation, and many more. With the increasing amount of data generated, Machine Learning is becoming an essential tool to extract insights and make predictions, improving the decision-making [[process]] and automation. | Machine learning can be used to solve a wide range of problems, from simple tasks such as image [[classification]], to more complex tasks such as natural language understanding, and autonomous systems. It can be applied in various industries such as healthcare, finance, manufacturing, transportation, and many more. With the increasing amount of data generated, Machine Learning is becoming an essential tool to extract insights and make predictions, improving the decision-making [[process]] and automation. | ||
[[Category:Quality 4.0]] | [[Category:Quality 4.0]] | ||
Revision as of 05:25, 20 March 2023
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Machine Learning (ML) is a subset of artificial intelligence (AI) that allows systems to learn and improve from experience automatically, without being explicitly programmed. Machine learning algorithms can be used to analyze data, learn from it, and make predictions or decisions.
There are several types of machine learning, including:
- Supervised learning: In this type of machine learning, the algorithm is trained using labeled data, where the output or correct answer is already known. The algorithm learns from the labeled data and can be used to make predictions on new, unseen data.
- Unsupervised learning: In this type of machine learning, the algorithm is not provided with labeled data. Instead, it must find patterns and structure in the data on its own. This type of machine learning is typically used for tasks such as clustering and dimensionality reduction.
- Reinforcement learning: In this type of machine learning, the algorithm learns through trial and error, by receiving rewards or penalties for certain actions. Reinforcement learning is commonly used in decision-making and control systems.
- Semi-supervised learning: This type of machine learning is a combination of supervised and unsupervised learning, where the algorithm is provided with some labeled data and some unlabeled data.
- Deep learning: This type of machine learning is a subset of neural networks which uses a multi-layer architecture and can be used for image and speech recognition, natural language processing, and other tasks.
Machine learning can be used to solve a wide range of problems, from simple tasks such as image classification, to more complex tasks such as natural language understanding, and autonomous systems. It can be applied in various industries such as healthcare, finance, manufacturing, transportation, and many more. With the increasing amount of data generated, Machine Learning is becoming an essential tool to extract insights and make predictions, improving the decision-making process and automation.
