This short article is not so much a guide as a good introduction to machine learning.
Machine learning (ML) is based on algorithms that can learn from data without relying on rules-based programming. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the need for more machine learning. The authors give a good illustration of what makes ML so radically different from non-ML programming:
In 2007 Fei-Fei Li, the head of Stanford’s Artificial Intelligence Lab, gave up trying to program computers to recognize objects and began labeling the millions of raw images that a child might encounter by age three and feeding them to computers. By being shown thousands and thousands of labeled data sets with instances of, say, a cat, the machine could shape its own rules for deciding whether a particular set of digital
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