Machine Learning Identified

Machine learning is a method of data analysis that enables systems, the ability to learn and improve from experience, rather than being programmed. Machine Learning is a branch of artificial intelligence, built on the notion that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Machine learning algorithms can be categorized into four different categories..

• Supervised algorithms – is a learning method that applies what has been learnt in the past to new data, using labeled examples to predict future event.
• Unsupervised algorithms – is a learning method that applies to information that is used to train, is neither classified nor labeled. 
• Semi-supervised algorithms- is a learning method that is a mix of supervised and unsupervised algorithms, since both use labeled and unlabeled data for training. In this algorithm, the ratio is usually a small amount of labeled data to a large amount of unlabeled data. This method is used to improve learning accuracy.
• Reinforcement  algorithms – is a learning method that interacts with its environment by producing actions and discovers errors or rewards. This a type of reinforcement learning.

By incorporating machine learning into a business, it can deliver fast and accurate results to identify opportunities or risks, however it does require additional time and resources before it can be used to its full potential.

In Data Defined, we help make the complex world of data more accessible by explaining some of the most complex aspects of the field.

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