Introduction¶

This introductory page is a big long, but that's because all the below concepts are common to every upcoming topic.

Machine Learning¶

Field of study that enables computers to learn without being explicitly programmed; machine learns how to perform task \(T\) from experience \(E\) with performance measure \(P\).

Machine learning is necessary when it is not possible for us to make rules, ie, easier for the machine to learn the rules on its own

flowchart LR

subgraph Machine Learning
    direction LR
    i2[Past<br/>Input] & o2[Past<br/>Output] -->
    a(( )) -->
    r2[Derived<br/>Rules/<br/>Functions]

    r2 & ni[New<br/>Input] -->
    c(( )) -->
    no[New<br/>Output]
end

subgraph Traditional Programming
    direction LR
    r1[Standard<br/>Rules/<br/>Functions] & i1[New<br/>Input] -->
    b(( )) -->
    o1[New<br/>Output]
end

Why do we need ML?¶

To perform tasks which are easy for humans, but difficult to generate a computer program for it

Stages of ML¶

flowchart LR
td[Task<br/>Definition] -->
cd[(Collecting<br/>Data)] -->
l[Learning<br/>Type] -->
c[Define Cost] -->
Optimize -->
Evaluate -->
Tune -->
save([Save Model]) -->
Deploy

ld[(Live <br/>Data)] --> Deploy

Open-source Tools¶

Entropy¶

Entropy, as it relates to machine learning, is a measure of the randomness in the information being processed. The higher the entropy, the harder it is to draw any conclusions from that information.

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