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Machine Learning

Solutions providing improved prediction and decision support based on data and learning.

Machine learning is an application of artificial intelligence (AI) that gives systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

Without human intervention or assistance

The process of learning begins with observations or data, such as examples, direct experience, or instruction, to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.

But using the classic algorithms of machine learning, text is considered as a sequence of keywords; instead, an approach based on semantic analysis mimics the human ability to understand the meaning of a text.

Machine Learning

Methods of Machine Learning

Supervised machine learning

Algorithms can apply what has been learned in the past to new data using labelled 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 can 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 to modify the model accordingly.

Unsupervised machine learning

Algorithms are used when the information used to train is neither classified nor labelled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabelled data. The system does not figure out the right output, but explores the data and can draw inferences from datasets to describe hidden structures from unlabelled data.

Semi-supervised machine learning

Algorithms that fall somewhere in between supervised and unsupervised learning, since they use both labelled and unlabelled data for training — typically a small amount of labelled data and a large amount of unlabelled data. The systems that use this method can considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labelled data requires skilled and relevant resources to train it / learn from it. Otherwise, acquiring unlabelled data generally does not require additional resources.

Reinforcement machine learning

A learning method 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 behaviour within a specific context to maximise its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.

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