Machine Learning
Machine learning enables computers to identify patterns in data and improve predictions without being explicitly programmed. As organisations collect increasing volumes of data, machine learning techniques allow analysts to automate analysis, uncover hidden relationships, and generate predictive insights that support evidence-based decision making.
Technique Overview
Machine Learning Definition
Machine learning is a field of artificial intelligence that uses algorithms and statistical models to identify patterns in data and improve predictions or decisions without explicit programming. By learning from historical data, machine learning systems can recognise relationships, classify information, and forecast future outcomes. Common approaches include supervised learning, which uses labelled data, unsupervised learning, which identifies hidden patterns in unlabelled data, and reinforcement learning, where models improve through feedback and rewards (Mitchell, 1997; Bishop, 2006).
Machine Learning Description *
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Business Evidence
Strengths, weaknesses and examples of Machine Learning *
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Business Application
Implementation, success factors and measures of Machine Learning *
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Professional Tools
Machine Learning videos and downloads *
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Further Reading
Machine Learning web and print resources *
Machine Learning references (4 of up to 20) *
- Mitchell, T.M. (1997) Machine Learning. New York: McGraw-Hill.
- Bishop, C.M. (2006) Pattern Recognition and Machine Learning. New York: Springer.
- Hastie, T., Tibshirani, R., Friedman, J.H. and Friedman, J.H., 2009. The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: Springer.
- Goodfellow, I., Bengio, Y., Courville, A. and Bengio, Y., 2016. Deep learning (Vol. 1, No. 2, pp. 1-800). Cambridge: MIT press.
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