Machine learning is one of those subjects that appears daunting in concept, but once someone is in a position to take a working example apart and rebuild it, it starts to feel like common sense. Fundamentally machine learning involves teaching a machine to recognize patterns in data and using the patterns identified to make predictions relevant to the task. The machine is never explicitly programmed with rules; it instead “learns” how to recognize patterns by observing examples and by itself refining how it finds patterns each time and gets progressively better at the task. The thousands of iterations through actual data produce the effect people refer to as “smart” software.
The good news is that it takes very little time (measured in hours) for someone who understands programming fundamentals, has a basic familiarity with data, and some time to read a bit, to build and evaluate an actual machine learning model. This tutorial describes the steps one takes, in chronological order of how they actually occur, with explanations for each in the overall context of ai and machine learning.
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