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Brief explanation of machine learning.

Updated: Aug 15



When most of the people hear “Machine Learning,” they picture a robot: a dependable butler or a deadly terminator counting on who you ask. But Machine Learning isn't just a futuristic fantasy, it’s already here.

What Is Machine Learning?

Machine Learning is that the science (and art) of programming computers in order that they can learn from data.

Here may be a slightly more general definition:

[Machine Learning is the] field of study that provides computers with the power to find out without being

explicitly programmed. By-Arthur Samuel, 1959

And a more engineering-oriented one:

"A computer program is claimed to find out from experience E with reference to some task T and a few

performance measure P, if its performance on T, as measured by P, improves with experience E. By-Tom Mitchell, 1997."



Why Use Machine Learning?


Consider how you'd write a spam filter using traditional programming techniques:

1. First, you'd check out what spam typically seems like . you would possibly notice that some words or phrases (such as “4U,” “credit card,” “free,” and “amazing”) tend to return up tons within the subject. Perhaps you'd also notice a couple of other patterns within the sender’s name, the email’s body, and so on.


2. you'd write a detection algorithm for each of the patterns that you simply noticed, and your program would flag emails as spam if a variety of those patterns are detected.


3. you'd test your program, and repeat steps 1 and a couple of until it's ok.




To summarize, Machine Learning is great for:


Problems that existing solutions require tons of hand-tuning or long lists of rules: one Machine Learning algorithm can often simplify code and perform better.

Complex problems that there's no good solution in the least employing a traditional approach: the simplest Machine Learning techniques can find an answer.

Fluctuating environments: a Machine Learning system can adapt OR always adapt to new data.

Getting insights about complex problems and enormous amounts of knowledge.

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