Machine Learning

Machine Learning is both a technology and a skill that allows a computer to perform a learning process without having been programmed to do so first. This technique, linked to the field of artificial intelligence, aims to highlight patterns and to develop statistical predictions.

Basically, it is a kind of program that allows a computer or machine to learn automatically so that a number of very complex operations can be performed.

Machine learning focuses on the development of computer programs that can access data and use it for themselves.

This technology aims to teach machines to learn from data and improve with experience, instead of being explicitly programmed to do so.

Components

The distinguishing point from traditional computer programs is that machine learning algorithms are not static: they change and adjust over time, capitalizing on each request and interaction.

Data mining, which consists of extracting information from a large amount of data, serves as the raw material for machine learning to reveal patterns for statistical prediction. This is why BigData is inseparable from Machine Learning. The larger the set of data processed that can be used to identify trends, the more accurate the predictions.

Machine learning is made up of three parts – the algorithm at the heart of the decision-making process, the variables and functionalities that make up the decision, and the basic knowledge for which the answer is known and which enables the system to learn.

The model is fed from the start with parameter data from known responses. The algorithm is then executed and adjustments are made until the output of the algorithm and the known response match. At this point, increasing amounts of data are entered to train the system and process more complex decisions.

The learning process begins with observations or data, such as examples, direct experience, or instructions, in order to look for patterns in the data and make better decisions in the future, based on the examples provided. The main goal is to enable computers to learn automatically without human intervention or assistance and to adjust actions accordingly.

Data is at the heart of business activity. Data-driven decision-making is an asset that keeps businesses in the game. Machine learning can play a key role in empowering data and customers and helping companies make the right decisions to stand out.

Categories

No matter how simple or complex, machine learning can be classified into three broad categories – machine learning with supervision, machine learning without supervision and machine learning by reinforcement.

Machine learning with supervision is basic but firm technology. Operators present the computer with examples of desired inputs and outputs, and the computer searches for solutions to get those outputs based on those inputs. The goal is for the computer to learn the general rule that maps inputs and outputs.

In unsupervised machine learning, the algorithm is left to its own devices to define the structure of the input (no label is given to the algorithm). This approach can be a goal in itself (which uncovers structures buried in the data) or a means to achieve a certain goal. This approach is also called “feature learning”.

In reinforcement machine learning, a computer program interacts with a dynamic environment in which it must achieve a certain goal, for example driving a vehicle or facing an opponent in a game. The apprentice program receives feedback in the form of “rewards” and “punishments” as he navigates the problem space and learns to identify the most effective behavior in the given context.

Machine learning concerns all sectors of activity, including industry, commerce, health and life sciences, tourism and hospitality, financial services, energy, raw materials, and utilities. Areas of use may include the industrial sector, commerce, health and life science, hotels and travel, financial services and energy. The areas where this may vary from monitoring of equipment to pricing. 

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