Machine Learning Tutorial
Machine Learning - Home
Machine Learning - Introduction
What Today’s AI Can Do?
Machine Learning - Traditional AI
What is Machine Learning?
Machine Learning - Categories
Machine Learning - Supervised
Machine Learning - Scikit-learn Algorithm
Machine Learning - Unsupervised
Artificial Neural Networks
Machine Learning - Deep Learning
Machine Learning - Skills
Machine Learning - Implementing
Machine Learning - Conclusion
Machine Learning Useful Resources
Machine Learning - Quick Guide
Machine Learning - Useful Resources
Machine Learning - Discussion

Machine Learning - Skills

Necessity of Various Skills of Machine Learning

To give you a brief idea of what skills you need to acquire, let us discuss some examples −

Mathematical Notation

Most of the machine learning algorithms are heavily based on mathematics. The level of mathematics that you need to know is probably just a beginner level. What is important is that you should be able to read the notation that mathematicians use 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 not, you may need to brush up your mathematics knowledge.

$$f_{AN}(net-\theta)=\begin{cases}\gamma & if\:net-\theta \geq \epsilon\\net-\theta & if - \epsilon< net-\theta

$$\displaystyle\\\max\limits_{\alpha}\begin{bmatrix}\displaystyle\sum\limits_{i=1}^m \alpha-\frac{1}{2}\displaystyle\sum\limits_{i,j=1}^m label^\left(\begin{array}{c}i\\ \end{array}\right)\cdot\:label^\left(\begin{array}{c}j\\ \end{array}\right)\cdot\:a_{i}\cdot\:a_{j}\langle x^\left(\begin{array}{c}i\\ \end{array}\right),x^\left(\begin{array}{c}j\\ \end{array}\right)\rangle \end{bmatrix}$$

$$f_{AN}(net-\theta)=\left(\frac{e^{\lambda(net-\theta)}-e^{-\lambda(net-\theta)}}{e^{\lambda(net-\theta)}+e^{-\lambda(net-\theta)}}\right)\;$$

Probability Theory

Here is an example to test your current knowledge of probability theory: Classifying with conditional probabilities.

$$p(c_{i}|x,y)\;=\frac{p(x,y|c_{i})\;p(c_{i})\;}{p(x,y)\;}$$

With 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

Here is an optimization function

Subject to the following constraints −

$$\alpha\geq0,and\:\displaystyle\sum\limits_{i-1}^m \alpha_{i}\cdot\:label^\left(\begin{array}{c}i\\ \end{array}\right)=0$$

If you can read and understand the above, you are all set.

Visualization

In many cases, you will need to understand the various types of visualization plots to understand your data distribution and interpret the results of the algorithm’s output.

Besides the above theoretical aspects of machine learning, you need good programming skills to code those algorithms.

So what does it take to implement ML? Let us look into this in the next chapter.

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