Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Simply put, Machine Learning algorithms learn the rules and trends through a hierarchy of concepts that describe the system from which predictions can be made. One obvious advantage is that Machine Learning removes the need for a programmer to painstakingly model the system through formally coded rules and logic. It is difficult to find the ceiling of Machine Learning capabilities. Terabytes, even petabytes, of data can be ingested into a Machine Learning pipeline whereby an A.I. is trained to draw predictions within an incomprehensible parameter space in a way that would otherwise be impossible.
For us to carry out our normal everyday lives requires a seriously immense amount of knowledge about the world. Similarly, in order for an artificial intelligence to perform intelligently, it needs to capture this kind of knowledge. And the subjective nature of a substantial portion of this knowledge makes it difficult to describe and prescribe the rules we use for interpretation and decision making.
Machine learning is already being applied to the business world. It has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.
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Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics.
The objective of Machine Learning is to emulate human intelligence, which tends to rely on Big Data. Analyzing Big Data is easier with the power and flexibility of cloud computing. The elasticity and availability of this high-performance computing environment is fueling a surge in artificial intelligence.
It is perhaps less well-known that Machine Learning is good at drawing predictions from even limited datasets; i.e., datasets that may be imbalanced and contain few data points. Significant advancements have been recently made to allow for Machine Learning to accommodate datasets with few examples or recordings of historical events.