The popularity of Artificial Intelligence algorithms has been steadily growing for several years. Using algorithms significantly improves the development of many industries, including marketing, gaming, business planning and even medicine. Depending on the solution you are looking for, different techniques are used.
AI algorithms can be divided on two classes: supervised and unsupervised algorithms. AI is based on a neural network that is designed to resemble the network of brain neurons. Some of the most common examples of Artificial Intelligence Algorithms include: Decision Tree Algorithms, Deep learning, Linear Regression algorithms. All these methods are used to process and collect data. The goal is to create a model that will predict one variable, based on the already known values of the other variables. Through this regression analysis, we collect data that will help us study the models and are useful for future predictions.
They are great for analyzing a very complex function that can be simplified to less complex generalizations. This allows the algorithms to find the best match based on comparing similarity with new instances of the query.
Decision Tree Algorithms
The algorithm works recursively for each node of the tree.
All tree nodes are a metaphor for the single data attributes of the model we are working on. The branches represent the values that correspond to them. Leaves testify to individual decisions.
Based on examples, the algorithm acquires knowledge – it finds different solutions to a specific problem and notices the relationships between them. Moreover, it predicts the values of the dependent (predicted) variable based on the values of the independent variables.
This method is based on grouping elements that belong to similar categories. This division depends on the similarity between the elements expressed by the similarity function.
This is used, for example, in the preliminary analysis of data and the extraction of sub-populations from them (for example, the division of customers into specific groups). They are then subjected to further analysis or data mining.
Association Rule Learning Algorithms
Association-based data mining (association discovery) relies on a specific set of attributes from the database for repeated elements. Such information may be useful for analyzing the contents of a shopping cart or creating offers for specific customer groups.
The algorithm for providing information about the events resulting from the function by determining the cause and effect.
Ensemble methods take many less effective learning elements and find a way to combine them into one overall output. Here the main starting point is how these algorithms are connected to each other.
Deep Learning Algorithms
These methods use larger models with a hierarchical structure composed of many nonlinear layers, according to the increasing complexity.
These algorithms are very effective in tasks such as object recognition or machine translation.
The process of this analysis involves several layers of data processing. The output of the last layer is used as input to the next one. The analysis process continues depending on the achievement of the expected level of accuracy.
These methods of applying algorithms are among the most popular. Researchers are constantly working on developing new methods. As you can see, each method is good and will work for specific queries, but it may also be useless for another problem. Artificial intelligence has changed and significantly developed the vision of predicting the probabilities of events.