MACHINE LEARNING – THE FUTURE IS AT OUR REACH

Machine Learning is a part of computer science, closely linked to Artificial Intelligence (AI), which from the application of algorithms can learn, establish patterns on the data, and make predictions on them.

The huge amount of information currently available on the Internet is raising some technologies that until recently were almost exclusively part of Universities, Research Centers and Large Companies. I refer to technologies related to Big Data, as well as those related to Machine Learning.

Table of Contents

What is Machine Learning?

What Are Its Practical Applications?

This somewhat futuristic definition and that a priori seems so far from our daily routine, has applications as current as:

Spam detection in emails.
Detection of fraud with credit cards.
Voice recognition.
Face detection to identify people.
Product recommendations on online sales websites.
Medical diagnoses to identify diseases based on their symptoms.
Customer segmentation to determine if a potential customer that is in a certain phase in the sales process will buy our product or service.
Prediction of home sales.
In cybersecurity, to detect attacks and mitigate their possible effects

The big companies are already using it to improve and enhance their services:

Google is developing a service called “ Smart Reply ” that will be integrated into Gmail and that will allow us to automatically respond to incoming emails. But there are already applications integrated into our devices, such as Google Now¸ that are already implementing these technologies for a long time :
Microsoft will integrate the services of “Azure Machine Learning” in its CRM Dynamics 2016 to help companies obtain a faster and more effective experience for customers, providing a knowledge management system that allows companies to register and access information in problem-solving. The system will learn continuously as the client-employee interaction grows.
Facebook through its AI team called FAIR (“Facebook AI Research”) is working on algorithms that allow users to display information much more selectively, and this, of course, includes filtering images and photos.

A Brief Review of The Machine Learning Algorithms

The first classification of the algorithms addresses the way in which data is presented to the learning system, and we can have:

Supervised learning :The perfectly defined and labeled input data is presented and the outputs we want to obtain are known. For example, given a set of images of animals labeled according to their breed, we want to predict to which animal breed a new image supplied to the system belongs.
Unsupervised learning :The input data is undefined, and the algorithm is left to find the structure and behavior patterns in them. Following the previous example, we can provide a set of images of animals without labeling the system, with the aim of establishing groupings based on similarity patterns between them.

The other classification addresses the function of the algorithm and what we expect from it. In this sense we can mainly have the following:

Regression: Try to model the relationships between variables through multiple iterations that are refined based on an error measure.For example, it could be applied to predict the price of a home taking into account multiple factors, such as the area, number of rooms, city, neighborhood, etc.
Classification: It is used to estimate discrete values (0/1, True / False, Yes / No) based on a set of independent variables. It is also known as “Logistic Regression.An example of the application would be the classification of an e-mail as spam, depending on the text, subject, issuer, etc.
Clustering: Try to find patterns in the structure of the data to organize them in a way that allows clustering by the greatest possible similarities.It could be applied, for example, to classify any type of article by subject according to its content: sports, science, literature, etc.
Recommendation: It seeks to predict the degree of preference that a user provides to any element. This is the case of the recommendation of products for online sales, based on previous purchases, preferences of previously visited items, history of other customers’ purchases, and even personal characteristics, such as sex, age, etc.
Deep Learning: They build larger and more complex neural networks to solve cases in which we have large volumes of data that may not be labeled, or partially. It is used for example in topics related to computer vision, for questions such as the classification of an image according to certain facets that can identify it.

Conclusion, The Future Is Here

We can take a look at the “State of the Art” in Machine Learning, with the sectors and companies already using these technologies. As mentioned, we are already using although these algorithms go unnoticed in many of the facets of our daily lives, The applications fully integrated with our devices, but their use will go much further, and researchers continue to work on algorithms every time more perfect. With the increasingly easy access to these technologies by anyone, the competitive advantages are so important that no technology company can neglect its use.

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