Data Science is about the distillation of knowledge from data. Somewhat more precisely, the discipline is dedicated to the scientifically sound, automated processing and analysis of data. The purpose is to extract valuable information from them for specific research interests, for example, to make predictions.
Machine learning or machine learning uses different methods from mathematics, including statistics, linear algebra, optimization theory and information theory. Machine learning is considered a branch of Artificial Intelligence since the ability to learn is a characteristic of intelligent systems. Ultimately, however, neither the terms nor the disciplines are completely clear.
Why machine learning for smart shopper?
Machine learning offers solutions for two major challenges. First, in many areas, due to the high complexity and diversity of the data, it is hardly possible to manually develop a suitable solution model. Often there are no analytical solutions or they can only be found with a great deal of effort.
Second, the “learned” model will typically produce better results than a manually developed model when encountered with new data sets. The ability to adapt – one also speaks of robustness – to new things is greater. Models based on machine learning, therefore, offer the advantage of either enabling complex analyzes in the first place or at least carrying them out much faster and more cost-effectively. When shopping online, it is good to activate card for cost-effective reasons.
Smart shopper: Cognitive technologies
In business, applications that are based on artificial intelligence and, among other things, use the self-optimizing machine learning algorithms described, are also called cognitive technologies. These technologies can be roughly divided into three fields of application, even if there is of course always an overlap. These are the areas of products & services, process optimization and knowledge acquisition, i.e. analyzes and predictions. In the Products & Services area, the main aim is to offer users greater convenience or to actively support them in everyday life.
The use of devices and services should generally be simplified and the consumer should be relieved by a system that is as adaptive as possible and that reacts to his or her individual behaviour.