You were dreaming about understanding AI and machine learning? Well, this article is made for you. We are going to demystify AI.

What is machine learning?

Before we go further in this article, we do need to define what is machine learning.

To summarize, machine learning is the fact of solving a problem without telling a computer how to solve it. What I mean by that is that in classic programming you would write code to explain to the computer how to solve a problem and explain to him what are the _different step_s to do it. With machine learning, the computer is using statistical algorithms to solve a problem by itself thanks to input data. It does this, by finding the patterns between the input and the output of the problem.

What do I need to do to do machine learning?

To do machine learning, you will need data, a lot of data.

When you do have these data, what you will need to do is to split your data into two datasets:

• The test dataset: the data you will use for testing if your machine learning model (algorithm) is properly working.
• The** training dataset:** the data you will use for training your machine learning model.

So remember, data is key, you need to have a proper amount of data, and clean these data (we will talk about how to clean your data for your dataset in another article).

What can I use machine learning for?

You can actually use machine learning for solving a lot of problems. Here are a few examples:

• Recommendations of products on e-commerce website (Amazon, eBay, …).
• Recommendations for a search engine website (Google, Facebook search, …).
• Netflix uses it as well to recommend movies and TV series depending on what you actually like.
• Youtube to put the subtitles under your videos, …

How can I teach machines to learn?

There are different ways for machines to learn, here are the four most popular ways:

• Supervised learning: your model will learn thanks to input labeled data that you provide to it (your data are already tagged with the correct labels). Which means that we show the correct answers to the machine. It can be used for classifying data, for example, classify cats by breeds.
• Unsupervised learning: your model will learn by observing. Which means that it will learn and improve by trial and error. In that case, we are not working with labeled data, so we don’t show the machine the correct answer. It can be used for clustering data, for example, group the loyal customers.
• Semi-supervised learning: your model starts with a small dataset and applies supervised learning (labeled data). Then we will feed the rest of the data to our model and observe them by applying unsupervised learning (non-labeled data). This will allow the computer to expand its vocabulary based on what it learned and classified during the supervised learning stage.
• Reinforcement learning: we train our model by rewarding it every time it has the correct output. Then the computer will try to get as many rewards as possible and will learn by itself. It can be used to create an AI for video games.

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