Machine learning: what is it and how does it work?

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Have you ever wondered how the voice assistant Siri can understand you? Or how Netflix recommends movies and series that you might like? Virtual assistants, digital platforms, and self-driving cars, among others, are practical examples of machine learning .

What is machine learning?
Machine learning means automatic learning. It is a branch of theartificial intelligence.

For machine learning to work properly, techniques are developed with the aim of learning computers. Machine learning aims to be an imitation of the brain and theneural connections.

It's easy to see it as something futuristic and even think of movies. But nothing could be further from the truth; we've been living among artificial intelligence for some years now. Believe it or not, we ourselves contribute to improving machine learning every day.

So, from now on we will not conceive the world without machine learning.

How does machine learning work?
machine learning

It all began around 1950 when Alan Turing proposed cameroon whatsapp lead the possibility of machines learning from experience. Turing invented the first computer program to play chess against a human. All this thanks to a complex system of choosing moves.

As the years went by and technology advanced, algorithms were improved. For the first time in 1997, a computer developed by IBM managed to defeat the chess champion.

Nowadays, behaviors are predicted by collecting a large amount of data and using algorithms. Machine learning improves over time . The more data it obtains, the more this technique becomes more refined.

Algorithms are therefore a fundamental part of machine learning. The role of algorithms is so important because they perform actions through what is defined.

In the specific case of machine learning, algorithms act on their own. They are designed to learn from experience and therefore to design new actions. This is why the more data they collect, the more precise and complex the responses will be.

They can also reach and please a large number of people due to the customization achieved.

Within machine learning, there are several types of algorithms. We explain the three most commonly used ones.

1) Reinforcement learning
The algorithm in this case learns from experience. It also learns from mistakes. It must acquire knowledge, through failure or success, of behavior.

The goal is to maximize success so that decisions are as optimal and accurate as possible.

An example to understand this type of learning in machine learning is any chess program. These try to achieve the best possible result: to win.

All this after failed attempts due to not choosing the best move.

2) Supervised learning
Using tags, you can detect patterns that will help you create relationships.
For example, let's think about a recipe website.

The classification could be by the labels of the ingredients that we need in each of them.

If we are interested in a recipe that uses lemon, we will surely be recommended other recipes with that same ingredient.

3) Unsupervised learning
Finally, this type of machine learning attempts to classify information on its own. That is why it will need to be able to recognize patterns in order to label.

An example is facial recognition that some smartphones have. They look for common patterns on the face for recognition and unlocking.