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Machine Learning is a scientific discipline that addresses the following question: “How can we program systems to automatically learn and to improve with experience? “Learning in this context is not learning by heart but recognizing complex patterns and make intelligent decisions based on data. The difficulty lies in the fact that the set of all possible decisions given all possible inputs is too complex to describe. To tackle this problem the field of Machine Learning develops algorithms that discover knowledge from specific data and experience, based on sound statistical and computational principles.
The field of Machine Learning integrates many distinct approaches such as probability theory, logic, combinatorial optimization, search, statistics, reinforcement learning and control theory. The developed methods are at the basis of many applications, ranging from vision to language processing, forecasting, pattern recognition, games, data mining, expert systems and robotics.
The history of the field of Machine Learning is a fascinating story. In 1946 the first computer system ENIAC was developed. At that time the word “computer” meant a human being that performed numerical computations on paper and ENIAC was called a numerical computing machine. This machine was manually operated, i.e. a human would make connections between parts of the machine to perform computations. The idea at that time was that human thinking and learning could be rendered logically in such a machine.
In 1950 Alan Turing proposed a test to measure its performance. The Turing test is based on the idea that we can only determine if a machine can actually learn if we communicate with it and cannot distinguish it from another human. Although, there have not been any systems that passed the Turing test many interesting systems have been developed.
Machine learning can easily consume unlimited amounts of data with timely analysis and assessment. This method helps review and adjusts your message based on recent customer interactions and behaviors. Once a model is forged from multiple data sources, it has the ability to pinpoint relevant variables. This prevents complicated integrations while focusing only on precise and concise data feeds.
Applying machine learning to practical applications and scenarios is simply vital. While predictive analytics are instrumental in saving costs and building revenue – it is equally as important to understand their impacts on real-life situations pertaining to customer acquisitions or loss.
Machine learning is proactive and specifically designed for “action and reaction” industries. In fact, systems are able to quickly act upon the
Outputs of machine learning: Making your marketing message more effective across the board. For example, newly obtained data may propel businesses to present new offers for specific or geo-based customers. However, data can also signify cutting back on unnecessary offers if these customers do not require them for conversion purposes.
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