Inductive learning, or learning based on empirical patterns and data, is what machine learning is. Machine learning is the process of performing training without the use of pre-made models.

Machine learning techniques require understanding in a variety of fields, including quantitative statistics ^*and analysis, optimization techniques, probability theory, numerical approaches, graph theory, etc. The training sample is a collection of events (things) and their accompanying outcomes (responses, reactions). It should be possible to create an algorithm that can respond realistically to any input signal (object). Utilizing the quality functional, such an algorithm's performance is evaluated.

Machine learning is frequently used in business for pattern recognition. For this, an artificial neural network is good example. Finding the coefficients of connections between neurons is the key to learning. The ability of a neural network to recognize intricate connections between inputs and outputs and to draw generalizations is its most significant ability. In order to recreate the image, the neural network can fill in the gaps.

Machine learning is used in manufacturing to solve a variety of typical tasks, including the following: production process optimization; equipment failure forecasting.

I provide a few examples for each component of the neural network training method's framework. Utilizing computer vision, production process optimization is achievable (pattern recognition). For instance, if a huge stone is not removed promptly, it may jam a crushing machine.

Equipment failure forecasting has many advantages. Equipment that is replaced on time will enable business operations to continue. As an illustration, consider the projection for replacing a section of pipeline, which is based on exploitation of both external and internal elements. Temperature, burial depth, environmental acidity and humidity, and other variables are examples of external factors. Pumping volumes, composition, pressure, and other internal parameters.

However, the production's owner should assist in finding solutions to some issues.

• Gathering of data. This requires digital sensors in modern production, as well as comprehensive and gathered information.
• Setting of priorities. The only people who can properly set priorities are business owners or employees.

***

Digitalization of production is inextricably linked with working with data, building forecasts and analysis.

by Iren Kolov,
Analyst at CounselSenses

^*https://strategylab.eu/statistics-for-strategy-building.html