What is Machine Learning?

Today the ability of machines to learn is present in many aspects of everyday life. Machine learning is behind the recommendations of movies on digital platforms, the ability to recognize the speech of virtual assistants or that of autonomous cars to see the road. Its origin as a branch of artificial intelligence dates back several decades.

Machine Learning

But What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The term was first used in 1959. However, it has gained prominence in recent years due to increased computing capacity and the data boom. Machine learning techniques are, in fact, a fundamental part of Big Data.

Machine learning is a very promising subfield of artificial intelligence, where systems have the ability to learn through data, statistics and trial and error in order to optimize processes and innovate at quicker rates. Machine learning is giving computers the ability to develop human-like learning capabilities that are allowing them to solve some of the world’s toughest problems, ranging from cancer research to climate change.

Computers no longer have to rely on billions of lines of code to carry out calculations. In classical computing, the only way to get a computer system to do something was to write an algorithm that defined the context and details of each action.

The algorithms used in the development of Machine Learning carry out many of these actions on their own. They get their own calculations based on the data that is collected in the system, and the more data they get, the better and more accurate the resulting actions will be.

Machine learning gives computers the power of tacit knowledge that allows these machines to make connections, discover patterns and make predictions based on what it learned in the past. Machine learning’s use of tacit knowledge has made it a go-to technology for almost every industry from fintech to weather and government.

Machine learning algorithms can process massive amounts of data and predict outcomes and patterns based on that information. Over time, the predictive model becomes more accurate as the program improves itself, no outside tampering required.

Machine Learning types

A computerized machine learning system uses experiences and evidence in the form of data, with which to understand patterns or behaviors by itself. In this way, you can make scenario predictions or initiate operations that are the solution for a specific task.

There are three main types of Machine Learning:

1. Supervised learning

This type of learning is based on what is known as training information. The system is trained by providing it with a certain amount of data, defining it in detail with labels.

Once you have been provided with enough of this data, you can enter new data without the need for labels, based on different patterns that you have been registering during the training. This system is known as classification.

Another method of development of Machine Learning consists of predicting a continuous value, using different parameters that, combined in the introduction of new data, allows predicting a certain result. This method is known as regression.

What distinguishes Supervised Learning is that different examples are used from which to generalize for new cases.

An example is a spam detector that labels an email as spam or not depending on the patterns you have learned from the email history.

2. Unsupervised learning

True values or labels are not used in this type of learning. These systems are intended to understand and abstract information patterns directly. This is a problem model known as clustering. It is a training method more akin to the way humans process information.

For example, in the field of marketing they are used to extract massive data patterns from social networks and create highly segmented advertising campaigns

3. Reinforcement learning

In the reinforcement learning technique, systems learn from experience. It is a technique based on trial and error, and on the use of premium functions that optimize the behavior of the system. It is one of the most interesting ways of learning for Artificial Intelligence systems, since it does not require the introduction of a large amount of information.

It is currently being used to enable facial recognition, make medical diagnoses, or classify DNA sequences.

Machine Learning Applications

Machine Learning is one of the pillars of digital transformation. Currently, it is already being used to find new solutions in different fields, including:

– Cybersecurity

– Search engines

– Smart vehicles

– Financial Services

– Healthcare

– Social media

– Retail and E-commerce

At Koukio Solutions we are experts in Machine Learning technology, contact us for more information.