wehub-resource-sync
38 lines
2.4 KiB
Markdown
38 lines
2.4 KiB
Markdown
# How would you explain machine learning to a software engineer?
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Software engineering is about developing programs or tools to automate tasks. Instead of "doing things manually," we write programs; a program is basically just a machine-readable set of instructions that can be executed by a computer.
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Let's consider a classic example: e-mail spam filtering. Assuming that we have access to the source code of our e-mail client and know how to handle it, we could come up with an instinctive set of rules that may help us with our spam problem.
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For example:
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if not "sender in contacts":
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if "subject line contains BUY!:
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e-mail spam folder:"
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else if ...
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It is intuitive to say that coming up with these rules is a pretty tedious task. Needless to say that we have to test our spam filter on real-world data, evaluate and improve it constantly, change and update rules, and so forth. Again, our goal is automation: we want to write a set of instructions that automatically filters out spam e-mails so that we don't have to "manually" delete them from our e-mail inbox.
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Now, **Machine learning is all about automating automation**! Instead of coming up with the rules to automate a task such as e-mail spam filtering ourselves, we **feed data to a machine learning algorithm, which figures out these rules all by itself.** . In this context, "data" shall be representative sample of the problem we want to solve -- for example, a set of spam and non-spam e-mails so that the machine learning algorithm can "learn from experience."
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In "conventional" programming, we code up a set of rules, feed it to the computer together with the data, and hope that it produces the desired results.
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**traditional programming:**
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- set of rules + data -> computer -> results
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In machine learning, we take data (e.g., e-mails), provide information about the desired results (spam and non-spam labels for these e-mails), and feed it to a learning algorithm, which in turn executed by a computer. The computer then *learns* a set of rules that we can use to automate (solve) our problem task.
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**machine learning:**
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- results + data -> machine learning algorithm + computer -> set of rules
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**Or in other words, machine learning is about finding the optimal instructions to automate a task. Machine learning algorithms are instructions for computers to learn other instructions automatically from data or experience. Therefore, machine learning is the automation of automation.**
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