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Machine Learning

This presentation will educate you about machine learning and discus on its types which are supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning,<br><br>For more topics stay tuned with Learnbay.

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Machine Learning

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  1. Machine Learning Swipe

  2. Machine Learning Machine learning (ML) is a form of artificial intelligence (AI) that allows software programmes to improve their prediction accuracy without being expressly designed to do so. In order to forecast new output values, machine learning algorithms use historical data as input.

  3. Machine Learning Example Machine learning is a branch of artificial intelligence (AI) that allows computers to learn and develop on their own without having to be explicitly programmed. Machine learning is concerned with the creation of computer programmes that can access data and learn on their own.

  4. Machine Learning Types Supervised learning Unsupervised learning Semi-supervised learning reinforcement learning

  5. Supervised learning Supervised learning, often known as supervised machine learning, is an artificial intelligence and machine learning subcategory. Its use of labelled datasets to train algorithms that properly categorise data or predict outcomes defines it.

  6. Unsupervised learning Unsupervised learning is a kind of machine learning in which models are trained on unlabeled data and then allowed to operate on it without supervision. This assignment will be completed by using an unsupervised learning method to cluster the picture dataset into groups based on visual similarities.

  7. Semi-supervised learning Semi-supervised learning is a machine learning technique that involves training using a small quantity of labelled data and a big amount of unlabeled data. Unsupervised learning (with no labelled training data) and supervised learning (with labelled training data) are the two types of learning (with only labelled training data).

  8. Reinforcement learning The training of machine learning models to make a series of judgments is known as reinforcement learning. In an uncertain, possibly complicated environment, the agent learns to attain a goal. An artificial intelligence encounters a game-like circumstance in reinforcement learning. Its objective is to increase the overall prize as much as possible.

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