# Defining Machine Learning

<mark style="color:blue;">**Machine Learning**</mark> is a field of <mark style="color:yellow;">**Artificial Intelligence**</mark> that deals with the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions without being explicitly programmed.&#x20;

<mark style="color:blue;">Machine Learning</mark> can be divided into three main categories: <mark style="color:purple;">**Supervised Learning**</mark>**,&#x20;**<mark style="color:orange;">**Unsupervised Learning**</mark>**, and&#x20;**<mark style="color:green;">**Reinforcement Learning**</mark>.&#x20;

<figure><img src="https://2628911669-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Ff4NOfl0B6H37C4qHRuSv%2Fuploads%2FRQ4ID5kGemM4bwtefBjp%2Fimage.png?alt=media&#x26;token=533dbc66-2ea2-42b5-90bb-d0a70c302aa6" alt=""><figcaption></figcaption></figure>

In <mark style="color:purple;">supervised learning</mark>, the algorithm is trained on a labeled dataset, and it learns to predict the output for new input data. In <mark style="color:orange;">unsupervised learning</mark>, the algorithm is not provided with labeled data and is trained to find patterns or relationships in the input data. In <mark style="color:green;">reinforcement learning</mark>, the algorithm learns by interacting with its environment and receiving feedback in the form of rewards or penalties. Machine Learning has a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, and predictive analytics.

{% hint style="info" %}
**Analogy:**&#x20;

An analogy that can be used to explain the definition of Machine Learning is to think of it as a child learning to play a game.

* Just like a child, a computer starts off not knowing how to do something (i.e. play a game).
* The child is given examples of how to play the game by a teacher or a parent (i.e. labeled data in Machine Learning).
* The child then practices playing the game and learns from its mistakes (i.e. the algorithm learns from the data).
* Eventually, the child becomes better at the game and can even come up with new strategies to win (i.e. the algorithm can make predictions or decisions without being explicitly programmed).
  {% endhint %}

#### References:

<https://ilmukomunikasi.uma.ac.id/2022/12/19/konsultan-machine-learning-valiance/>
