Machine learning beginning to changing the way we live. And even if you think you have not encountered this technology yet, you should know that it is already everywhere. In fact, you have found it in the recommendations of Netflix and also in Google.

The concept of machine learning is simple. But what if we investigate further its functions, characteristics and advantages?

One of the news related to this topic came from Apple. And most likely, you've heard of her.

The brand bought the machine learning firm Perceptio Inc , in an attempt to incorporate advanced artificial intelligence. He perceived the development of facial recognition algorithms that were running on users' devices without resorting to the cloud. The idea is that it could classify images on smartphones and reduce data overload that, in general, requires conventional methods.

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Pay attention to this data. According to a Statista report 'What part of your IT budget for 2017 is earmarked for machine learning?', 26% of companies recognize that more than 15% of their IT budget has been invested in the development of machine learning solutions.

Machine learning is beginning to change the way we live, and it is time for us to understand what it is and why it is important.

Delving into machine learning


Before entering machine learning, it is convenient to clarify a frequent doubt. Machine learning and artificial intelligence are not the same . In fact, machine learning is presented as an approach to the broader field of artificial intelligence .

Artificial intelligence is a simulation of intelligence in computers. As such, it is something that specialists have been working on for decades and encompasses a wide range of approaches.

Machine learning, meanwhile, is a form of artificial intelligence in which the computer learns itself to complete a task.

There are different ways to make the machines learn by themselves. At the core of deep learning are " artificial neural networks ." And the thought behind these neural networks forms what you will see next:

As you know, the best learning system is the human brain. That is why machine learning emerges as an attempt to imitate the way in which the brain learns. Neural networks become that attempt.

That is, they are mathematical representations of the way in which the human brain operates.

Types of machine learning
1.- Supervised learning
This type of machine learning works on previously tagged databases. For example, a computer has the ability to differentiate a photograph of a motorcycle from another and a bicycle.

For this, it is recommended that the labels be placed by a person. This guarantees the effectiveness and quality of the exercise.

The idea of ​​this type of machine learning is that computers train their "intelligence " from past examples. In this way, they can then perform all the calculations that are proposed so that a human being does not intervene and work can be expedited.

As most recognized examples you can see:

Voice recognition.
Spam detection
Also the recognition of writing.
2.- Unsupervised learning
This second category works as follows. The algorithm dispenses with any label. That is to say that it does not have the help of any previous indication.

However, it is awarded a large amount of data with the specifications that each object has. Thus, it is easier to detect what it is based on the information that has been previously stored.

3.- Reinforcement learning
The last type of machine learning has as a reinforcement engine. That is, the machine has the ability to learn based on different tests and errors given in different contexts.

Regardless of whether you know the results from the start, the machine still does not know what are the best decisions to achieve the correct results. In this case, the algorithm, by itself, associates the success patterns. And after repeating them constantly get the exact and infallible results.

The clearest example may be the navigation of an automatic vehicle.

Companies that already incorporate machine learning
1.- Facebook


One of the places where you will also have encountered machine learning is in the social network par excellence. Facebook uses its facial recognition algorithm to take your job off when tagging your friends in photographs.

It is an algorithm that memorizes certain patterns that define the faces of those people you have previously tagged to, thus, identify them automatically later.

2.- Pinterest
However, Facebook is not the only platform that has incorporated these technological advances. Even if you don't expect it, Pinterest also uses machine learning to boost engagement with users. But how?

The platform also employs an algorithm that learns from user activity. Thus, it manages to capture each one's favorite content based on their interests, tastes and habits.

3.- Paypal
Surely you already know Paypal, the payment platform that becomes an electronic wallet. And as such, you need to have a high level of security and trust from your users.

To remain one of the safest platforms, PayPal uses machine learning to identify and address fraud . Analyze large amounts of customer data and then segment them according to their activity.

4.- Cortana and Siri


There is no doubt that it has been seen that iPhone has caused several changes in the last decade. But he has not been the only one.

You see, the voice recognition systems of virtual assistants like Cortana and Siri also make use of machine learning. Although they use other technologies such as neural networks and natural language processing. In this way they manage to simulate that almost human conversation.

But how are these advances generated? How do you get machine learning to train?

In this case you need to work to recognize human language. And for this, several techniques with different levels of complexity are used. In the case of Siri, templates that resemble human behavior are used. That's why he has the ability to understand jokes.

5.- Google
There is no doubt that Google is one of the largest portals capable of collecting all types of user data. And as such, do you think I could do without machine learning?

The answer is not only a resounding no. But also, you should know that the Internet giant uses machine learning algorithms in all its services, starting with web search optimization. In fact, it studies some neural network that manages to detect the intention of each user.

But beyond Google Search , services like Gmail also make use of machine learning. Especially for your intelligent response services.

On the other hand, Google Maps has the ability to detect street numbers and names from Street View images.

6.- Netflix


Netflix, among other things, has managed to become a solid brand for its system of recommendation and customization of series and movies.

Thanks to machine learning, the platform applies the technology to:

The promotion of the content.
Your pricing model.
To programmatic marketing.
The delivery of the content.
7.- Uber


A few years ago nobody imagined that when ordering a taxi (using a mobile application), it would indicate the arrival time and the estimated price of your entire trip. Not much less, that would have a personalized food service to liven up the tour.

Uber does it thanks to machine learning, since it carries out a very specific study of the travel and order data of each user.

8.- Spotify


The weekly Spotify discovery is one of the examples of machine learning. The platform uses several recommendation models depending on the activity of each user. But it also does it thanks to a more exhaustive analysis of texts and audios that manage to group the likes, always similar, of others, to recommend them.

9.- Amazon
Amazon is another company that uses machine learning to improve its services.

In its search service, the product catalog is structured according to customer preferences.
In its recommendation system, the product catalog is organized according to interests.
And finally, it also faces the payment fraud prevention system.
However, the value of machine learning depends on the volume of communication of each organization. In addition, it also varies based on users and the possible channels through which these services can be implemented.