One of the hottest technologies utilized by data scientists or ML professionals to launch a real-time project is machine learning. To solve problems in the real world and create better products, you need more than just machine learning skills; you also need to become well-versed in data structures.

The data structure employed in machine learning is relatively comparable to those employed in other software development disciplines.

Data structure aids in the creation and comprehension of these challenging issues. In addition, knowing the data structure enables you to construct ML models and algorithms much more quickly than other ML experts.

Data structure in Artificial intelligence:

The priority queue is one  data structure that is used in artificial intelligence and it is  frequently employed in numerous search tasks. Using ascending order sorting, priority queues are used to hold a succession of objects (data). When an item is inserted into the priority queue, the next item to be checked is recorded and the priority queue keeps track of the ordering of each node in the stack that corresponds to an item. This data structure is typically implemented using heaps. When employing acceptable heuristics to identify the shortest paths in graphs, the search algorithm makes extensive use of this data structure. To decide which node to verify next in the process, the priority queue gives each node a weight.


This article has covered the role that data structures have in the development of machine learning algorithms. The majority of computing problems can be solved using data structures, thus learning about them and applying the best algorithm will give you the best and most effective answer for an ML problem. Additionally, a solid understanding of data structures will enable you to lay a solid foundation and leverage your talents to develop a superior machine learning project.

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