# Machine Learning - Skills

A Machine Learning expert requires skills across several domains. The skills that you need to acquire are listed below.

• Statistics

• Probability Theories

• Calculus

• Optimization techniques

• Visualization

## Necessity of Various Skills of Machine Learning

Here are some examples of what skills you need to acquire:

Mathematical Notation

Machine learning algorithms are heavily based on mathematics. The level of mathematics you need to know is probably just a beginner level. It is important that you understand the notation used by mathematicians in their equations. For example - if you are able to read the notation and comprehend what it means, you are ready for learning machine learning. If you cannot, you may need to brush up your mathematics knowledge.

${f}_{AN}\left(net-\theta \right)=\left\{\begin{array}{ll}\gamma & if\phantom{\rule{mediummathspace}{0ex}}net-\theta \ge ?\\ net-\theta & if-?

$\phantom{\rule{0ex}{0ex}}\underset{\alpha }{max}\left[\begin{array}{c}\sum _{i=1}^{m}\alpha -\frac{1}{2}\sum _{i,j=1}^{m}labe{l}^{\left(\begin{array}{c}i\end{array}\right)}\cdot \phantom{\rule{mediummathspace}{0ex}}labe{l}^{\left(\begin{array}{c}j\end{array}\right)}\cdot \phantom{\rule{mediummathspace}{0ex}}{a}_{i}\cdot \phantom{\rule{mediummathspace}{0ex}}{a}_{j}⟨{x}^{\left(\begin{array}{c}i\end{array}\right)},{x}^{\left(\begin{array}{c}j\end{array}\right)}⟩\end{array}\right]$

${f}_{AN}\left(net-\theta \right)=\left(\frac{{e}^{\lambda \left(net-\theta \right)}-{e}^{-\lambda \left(net-\theta \right)}}{{e}^{\lambda \left(net-\theta \right)}+{e}^{-\lambda \left(net-\theta \right)}}\right)\phantom{\rule{thickmathspace}{0ex}}$

Probability Theory

Test your probability theory knowledge with this example: Classifying with conditional probabilities.

$p\left({c}_{i}|x,y\right)\phantom{\rule{thickmathspace}{0ex}}=\frac{p\left(x,y|{c}_{i}\right)\phantom{\rule{thickmathspace}{0ex}}p\left({c}_{i}\right)\phantom{\rule{thickmathspace}{0ex}}}{p\left(x,y\right)\phantom{\rule{thickmathspace}{0ex}}}$

By using these definitions, we can define the Bayesian classification rule−

• If P(c1|x, y) > P(c2|x, y) , the class is c1 .

• If P(c1|x, y) < P(c2|x, y) , the class is c2 .

Optimization Problem

$\phantom{\rule{0ex}{0ex}}\underset{\alpha }{max}\left[\begin{array}{c}\sum _{i=1}^{m}\alpha -\frac{1}{2}\sum _{i,j=1}^{m}labe{l}^{\left(\begin{array}{c}i\end{array}\right)}\cdot \phantom{\rule{mediummathspace}{0ex}}labe{l}^{\left(\begin{array}{c}j\end{array}\right)}\cdot \phantom{\rule{mediummathspace}{0ex}}{a}_{i}\cdot \phantom{\rule{mediummathspace}{0ex}}{a}_{j}⟨{x}^{\left(\begin{array}{c}i\end{array}\right)},{x}^{\left(\begin{array}{c}j\end{array}\right)}⟩\end{array}\right]$

Here is an optimization function

Subject to the following constraints −

$\phantom{\rule{0ex}{0ex}}\underset{\alpha }{max}\left[\begin{array}{c}\sum _{i=1}^{m}\alpha -\frac{1}{2}\sum _{i,j=1}^{m}labe{l}^{\left(\begin{array}{c}i\end{array}\right)}\cdot \phantom{\rule{mediummathspace}{0ex}}labe{l}^{\left(\begin{array}{c}j\end{array}\right)}\cdot \phantom{\rule{mediummathspace}{0ex}}{a}_{i}\cdot \phantom{\rule{mediummathspace}{0ex}}{a}_{j}⟨{x}^{\left(\begin{array}{c}i\end{array}\right)},{x}^{\left(\begin{array}{c}j\end{array}\right)}⟩\end{array}\right]$

All you need to do is read and understand the above.

Visualization

To interpret the results of the algorithm, you need to understand the various types of visualization plots.

You need good programming skills to code those algorithms in addition to the above theoretical aspects of machine learning.

In the next chapter, we will examine what it takes to implement ML.

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