Descriptive analysis is the analysis of historical data to determine what is, what has changed, and what patterns can be identified.
Considered the most basic type of analysis, descriptive analysis involves breaking down big data into small pieces of usable information, so that companies can understand what is happening in a given business, process or set of operations. Descriptive analytics can provide insight into current customer behaviors and business trends to support decisions about resource allocations, process improvements, and overall performance management. Most industry observers believe that descriptive analysis represents a large part of the logical analysis used by companies today.
NLP tokenization (NLP Tokenization) is the process of dividing raw text into “tokens”, which are small units that can be processed by machine learning models in natural language processing.
Bayesian Networks are one of the most widely used types of probabilistic graphical models. Providing effective solutions for decision making and inference under uncertainty, these networks play a critical role in artificial intelligence, machine learning and data analysis.
Large Language Models (LLMs) are artificial intelligence models trained with millions of parameters that can perform language understanding and generation from large amounts of textual data. These models are considered a revolutionary step forward, especially in the field of natural language processing (NLP).
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