Prior to general machine learning, if we wanted to, for example, build a spam filter, we could start by compiling a list of words that commonly appear in spam. The spam detector then scans each email and when the number of blacklisted words reaches a threshold, the email would be classified as spam. This is called a rules-based approach, and is illustrated in the following diagram:
The problem with this approach is that once the writers of spam know the rules, they are able to craft emails that avoid this filter. The people with the unenviable task of maintaining this spam filter would have to continually update the list of rules. With machine learning, we can effectively automate this rule-updating process. Instead of writing a list of rules, we build and train a model. As a spam detector, it will be more accurate since it can analyze large volumes...
