With the Select data for predictive analytics and machine learning training, you will be able to:
You will be able to revolutionize your preventive maintenance activities to become efficient predictive, improving asset uptime, through the reduction of failure rates and asset downtime for preventive work, minimizing the risk to the business and the operational costs.
Predictive analytics can be defined as the procedure of condensing huge volumes of data into information that humans can understand and use, while 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.
Basic descriptive analytic techniques include averages and counts. Descriptive analytics based on obtaining information from past events has evolved into predictive analytics, which attempts to predict the future based on historical data.
Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.
The participants will attend a theoretical training session in Select data for predictive analytics and machine learning and a workshop that will allow the participants to identify the processes that can become paperless and a roadmap development.
Select data for predictive analytics and machine learning training workshop will be performed using a benchmark example.