As a subset of artificial intelligence (AI), machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions.
Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly.
Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.
There are several machine learning methods that have to do with algorithms. Supervised machine learning algorithms, for example, can apply what has been learned in the past to new data using labeled examples to predict future events.
Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
There’s also unsupervised machine learning algorithms, which are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data.
The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
Sometimes machine learning algorithms fall somewhere between supervised and unsupervised learning. This is referred to as semi-supervised machine learning because the algorithms use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data.
The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it.
One other method, reinforcement machine learning algorithms, is a learning modality that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance.
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Artificial Intelligence (AI) and Machine Learning (ML)
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