MACHINE LERANING

What is Machine Learning and what are its main applications?

by | Feb 13, 2018 | MachineLearning

Virginia Frías

Virginia Frías

Content & Social Media Manager

Do you know what is Machine Learning?

Machine Learning is not something new. However, the machine learning of today has little to do with the automatic learning solutions of the past.

Today, thanks to the development of new computer technologies, it is possible to apply algorithms to volumes of data that grow and vary at incredible speed. These models are able to modify themselves and make it possible to perform various tasks like develop recommendation programs online to, for example, drive vehicles autonomously.

Undoubtedly, this science is gaining impetus, but what is its basis?

What is Machine Learning?

In a general sense, we could say that Machine Learning is a type of Artificial Intelligence aimed at developing techniques so that machines can learn and make decisions for themselves.

This learning is possible thanks to the detection of patterns within a set of data so that it is the program itself that predicts what results can or cannot be achieved. These calculations are what allow them to learn to finally generate reliable decisions and results.

Supervised Machine Learning and Unsupervised Machine Learning

Machine Learning is a very broad field. Its rapid expansion is also making it continually divided into different specialties, among which we can highlight:

  • Supervised Machine Learning. This is the most used and requires human intervention for the creation of labels in the historical data so that the machine can predict a likely result from them. This method is used, for example, for the prediction of possible claims in customer service systems.
  • Unsupervised Machine Learning. Unsupervised learning is less common and uses historical data that has not been labeled. The goal is to find patterns from the data analysis itself. A very frequent use is the segmentation of customers with similar tastes for marketing campaigns.

As a result, what we get are high-value predictions that result in intelligent actions in real time.

How does Machine Learning impact business?

In short, it could be said that its impact is and will be enormous. In the short term it is most likely that Machine Learning will continue to be used as an acquired solution. However, in the long run it will be highly likely to find machine learning technology tailored to the needs of each company.

There are certain sectors where Machine Learning is key to decision making. In the surgical field, for example, it would be very useful in deciding whether to carry out an operation based on the success rate and the personal characteristics of previous patients. In the field of business, its applications are of the most diverse and can range from allowing businesses to establish which dates are better to raise or lower prices according to the demand, to estimating if the rhythm of sales is optimal at any given time.

Machine Learning is a very broad field. Its rapid expansion is also making it continually divided into different specialties

Machine Learning Applications

Machine Learning basically counts on as many applications as we can imagine because it is able to adapt to as many situations as data with which we are using.
Search engines, medical diagnostics, speech and language recognition, robotics etc.

Among others, these are just some of the activities of our daily life that are driven by machine learning:

  • Face Recognition. We can see this on our mobile cameras.
  • Facial, voice or object recognition.
  • Search engines. To improve searches and search suggestions.
  • Anti-spam. Through the use of labels.
  • Anti-virus. For the detection of malicious software.
  • Genetics. For example, in the classification of DNA sequences.
  • Prediction and forecasts. Of weather, traffic or to avoid technological malfunctions in equipment.
  • Reading Comprehension. Is applied to structured summaries of news or commentary on a specific topic.
  • Autonomous vehicles and robots.
  • Faster and more flexible optimization methods. Determines which moment is appropriate for any given task.
  • Analysis of high quality images.
  • Analysis of economic data. To operate in the stock market or to avoid fraud in transactions.
  • Analysis of consumer behavior and productivity. For the identification of potential customers, anticipate which employees can be more profitable and to adapt services to the needs of the user.

Machine Learning is especially effective in problems of a complex nature in which the application of algorithms helps to obtain precise solutions and, of course, with the consequent saving of time that this method implies.

At Intelygenz we are already using Machine Learning in several innovation projects for several of our clients. See how we can help you with this technology. You’d be surprised.

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