The Basics of Machine Learning

If you read all those books and looked a little bit around the internet you would probably be able to know what is machine learning but for me, I like the Arthur Samuel definition: “ A field of study that gives computers the ability to learn without being explicitly programmed”, In summary, machine learning is a sub-field of artificial intelligence, where we design systems that can learn from a provided data by training it.

There are 4 types of machine learning but two of them are the most used, Supervised, and unsupervised learning.

It is basically when you know the output so working with a set of labeled data, let’s say a classic example is to classify email messages into spam and non-spam you basically feed the algorithm with the input and the output and based on it the algorithm would eventually predict a class out of a never seen data based on experience.

The most used supervised algorithms are:

On the other hand, you have unsupervised learning, in which you let the algorithm learn on its own, formally let the algorithm find a hidden pattern in a load of data, there is no right or wrong answer, you are just training it and looking for the patterns it generates.

Unsupervised learning algorithms apply the following techniques to describe the data:

You would eventually come across two other types of machine learning That are getting more and more attention, read carefully.

The most used algorithms are:

The dataset contains both labeled and unlabeled examples. Usually, the quantity of unlabeled examples is much higher than the number of labeled examples. The goal of a semi-supervised learning algorithm is the same as the goal of the supervised learning algorithm.

A subfield of machine learning where the machine “lives” in an environment and is capable of perceiving the state of that environment as a vector of features. The machine can execute actions in every state.