Systems learning from patterns in data and making smart decisions
Today data is abundant; using self-learning algorithms from the field of machine learning and AI, we can turn this data into knowledge and actionable insight. Thanks to the many powerful open source libraries that have been developed in recent years, there has probably never been a better time to break into the machine learning field and learn how to utilize powerful algorithms to spot patterns in data and make predictions about future events. We will discuss and use the main concepts and different types of machine learning. Together with a basic introduction to the relevant terminology, we will lay the groundwork for successfully using machine learning techniques for practical problem solving.
Geoffrey Hinton (2006) trained a system capable of recognizing handwritten digits with precision (>98%). Training a neural net was widely considered impossible at the time and most researchers had abandoned the idea since the 1990s. Today neural nets are making mind-blowing achievements that no other Machine Learning (ML) technique could hope to match (with the help of tremendous computing power and great amounts of data).
Machine Learning and AI has conquered the world. It powers much of everything we do with technology.
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