Before machine learning algorithms became mature, many software application decisions were rule-based, consisting of a bunch of if and else statements to generate the appropriate response in exchange to some input data. A commonly cited example is a spam filter function in email inboxes. A mailbox may contain blacklisted words defined by a mail server administrator or owner. Incoming emails have their contents scanned against blacklisted words, and should the blacklist condition hold true, the mail is marked as spammed and sent to the Junk folder. As the nature of unwanted emails continues to evolve to avoid detection, spam filter mechanisms must also continuously update themselves to keep up with doing a better job. However, with machine learning, spam filters can automatically learn from past email data and, given an incoming email, calculate...
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