Above all, Artificial Intelligence’s tremendous growth in the past few years has shown its huge impact on the world’s economy. As AI continues to grow day by day, its adoption in every sector like robotics, agriculture, healthcare, marketing, and finance is becoming clearer. Also, computers are a faster resource when it comes to analytical abilities and calculations. Yet, the one drawback that keeps computers from overtaking humans is their inability to take decisions on their own. Types of Artificial Intelligence Algorithms allow computers to imitate human-like activities. Moreover, these special algorithms are capable of finding patterns but also coming up with a process to make a decision.
 
Machine learning is a subfield of AI – machines use inputs and by doing mathematics logics, generate output. However, Artificial Intelligence Algorithms use both output and input to generate new data output after getting new inputs.

Types of Artificial Intelligence Algorithms

One of the integral parts of the Artificial Intelligence Algorithm is to choose the accurate machine learning technique to solve any task. Since there are many algorithms in the Tech field, many organizations are already benefiting from it in a variety of ways.
Accordingly, many different types of algorithms can be used to solve problems. Therefore, let us have a closer look at the types of AI algorithms.

1. Classification Algorithms

Classification Algorithms fall under the ‘Supervised Machine Learning’ category. It divides the subjected variable into different classes and then predicts a class for a given input.
Thus, this classification comes into play whenever there is a need to predict an output from a set number.
Below are some of the used classification algorithms.

Naive Bayes

This algorithm follows a probabilistic approach and has a set of prior probabilities for each class. Moreover, these algorithms are ultra-fast and are most used in filtering ‘spam’.

Decision Trees

Decision trees are usually used like flow charts; where nodes represent the test on an input attribute and branches signify the outcome of the test.

Random Forest

In this algorithm, the given input is subdivided and fed into different decision trees. Then, the outputs from all decision trees are considered. In a nutshell, a random forest is like a group of different trees. Therefore, it is more precise than decision tree algorithms.

Support Vector Machines

Support Vector Machines algorithm classifies data by using the hyperplane. In other words, it tries to ensure the greatest margin between hyperplane and support vectors.

K-Nearest Neighbors

In the KNN algorithm, all bunches of data are segregate into different classes to predict the class of new sample data. Further, it refers to a ‘lazy learning algorithm’ since it is short as compared to other algorithms.

2. Regression Algorithms

Regression algorithms come into the supervised machine learning category. Firstly, these algorithms can predict the output values based on input data fed into the learning system. Besides, the most used regression algorithms’ applications include predicting the weather and predicting stock market price.
 
Algorithms use in ‘Regression Algorithms’ are as follows.

Linear Regression

Linear regression algorithm draws a straight line between different data points and by using the best-fit line, it predicts the new values.

Lasso Regression

Lasso regression algorithm obtains the subset of predictors that minimizes the error of prediction for a response variable.

Logistic Regression

Binary Classification is for logistic regression. Additionally, It allows the analysis of a set of variables as well as predicting an accurate output.

Multivariate Regression

Multivariate regression algorithms are useful when there is more than one predictor variable. However, this algorithm to be used for retail sector product recommendation engines.

Multiple Regression Algorithm

Multiple Regression Algorithm is a combination of linear regression and non-linear regression that takes many explanatory variables as an input.

3. Clustering Algorithms

Clustering Algorithms are a part of unsupervised machine learning. These algorithms separate and organize the data into different groups. Similarly, the main aim of these algorithms is to cluster similar items in a group where it’s more efficient to process any task.
 
The following are the different algorithms used in Regression Algorithms.

K-Means Clustering

This simplest unsupervised learning algorithm gathers similar points and links them together into a cluster. Moreover, the “K” in K-Means represents the number of clusters the data points are being grouped into.

Fuzzy C-Means Algorithm

This algorithm works on probability. Each data point will have a probability that belongs to another cluster. Plus, it refers to “fuzzy” as data points don’t have an absolute membership over a particular cluster.

Expectation-Maximization Algorithm

The expectation-Maximization algorithm is on the concept of Gaussian distribution. To solve the problem, data display in a Gaussian distribution model. Once the probability is assigned, a point sample is considered based on maximization equations.

Hierarchical Clustering Algorithm

These algorithms can be of two types:

· Divisive clustering – for a top-down approach

·  Agglomerative clustering – for a bottom-up approach

 
After making similar observations and learning the data points. The Hierarchical Clustering Algorithm sorts clusters in hierarchical order.
 

Conclusion

AI has several applications to solve complex problems. Today, we have shed some light on the multiple types of Artificial Intelligence algorithms and their broad classifications. However, every algorithm has its pros and cons when it comes to accuracy, performance, and processing time. Importantly, these Algorithms are in use for many areas of computing. So, they will get a financial market of their own in the next years.