Artificial Intelligence Algorithms

Artificial Intelligence is a machine language where a machine can act and think like a human being. Artificial Intelligence has a huge impact on the world’s economy and its increasing every day with tremendous growth. Since AI is growing day by day, its uses are in every field like Robotics, Agriculture, Healthcare, Marketing, Finance and many more. There are different kind of Machine learning that is:-Supervised Learning, Unsupervised Learning, Reinforcement Learning, and Ensemble Learning

In Machine Language, machines use some inputs and by doing mathematics logics, it produces output. Machine Language is known as the subfield of Artificial Intelligence. But Artificial Intelligence Algorithm uses both output and input at the same time in order to produce new data output after getting new inputs.

Types of AI Algorithms

One of the main parts of the Artificial Intelligence Algorithm is selecting the correct machine learning technique and method to solve any task. There are many algorithms in the field of Tech by using which, organizations and different sectors are getting benefits in different ways. Different kinds of Algorithms can be used to solve different problems. Below are the different types of Artificial Intelligence Algorithms.

1. Classification algorithms

Classification Algorithms come into the category of Supervised Machine Learning, where to start the process, data sets need some classes. In Classification Algorithms, the algorithms used to divide the subject variable into different kinds of classes to predict the classes for a given input. The main aim of the classification Algorithm is to compute the category of data. For Example-Classify emails as spam or not. Different algorithms used in Classification Algorithms are as follows.

Naive Bayes

This kind of algorithm follows the Bayes theorem, which follows a probabilistic approach. For each class, the algorithm has a set of prior probabilities. These algorithms are super-fast and are commonly used in spam filtering.

Decision Trees

Decision trees are commonly used like Flow charts.

Random forest

It is a group of different trees where. The given input is subdivided and fed into different decision trees. The solution of data is the average output of all decision trees. These algorithms are more accurate as compared to Decisions.

Support vector machines

Support Vector Machines are unique that classifies data by using the hyper plan. It tries to sort the data with the maximum margin between two classes.

K-nearest neighbors

In this algorithm, all bunches of data are separated into different classes to get new sample data.

2. Regression algorithms

In Regression algorithms, algorithms can predict the output values based on input data that are fed in the learning system. It is a popular algorithm that comes under the category of supervised machine learning algorithms. Some of the most used applications of regression algorithms are predicting the weather, predicting stock market price, etc. Different algorithms used in Regression Algorithms are as follows.

Linear Regression

It is the most simple and effective kind of Algorithm. This algorithm draws a simple line between different data points and by using the best fit line, predict the new values.

Lasso Regression

Lasso Regression is works on obtaining the subset of predict vales that minimizes the error of Prediction in a response variable.

Logistic Regression

Binary Classification is used in Logistic regression. Predicting customer lifetime value, house values are some examples of this algorithm. 

Multivariate Regression

When there are more predictor variables, A Multivariate regression algorithm is used.

Multiple Regression Algorithm

It is a combination of linear regression and non-linear regression.

3. Clustering algorithms

Clustering Algorithms comes under the category unsupervised Machine learning. The process of these kinds of algorithms is to segregate and organize the data into different groups based on their similarities within the same members of the group. Thus in this Algorithm, the main aim is to group similar items in a group where it is easy and more efficient to process any given task. For example, during any fraudulent activity in the debit cards or credit cards, all fraudulent activities can be arranged easily. Different algorithms used in Regression Algorithms are as follows.

K-means clustering

In this algorithm, it gathers similar points and binds them together into a cluster where K stands for different clusters. K-Means Clustering is the simplest unsupervised learning algorithm

Fuzzy C-means algorithm

Fuzzy C-means Algorithm works on Probability. It stands for FCM.

Expectation-Maximization algorithm

This Algorithm is based on Gaussian distribution.

Hierarchical clustering algorithm

There are two types of this algorithm, Divisive clustering, for a top-down approach and Agglomerative clustering, for a bottom-up approach. After learning the data points and making similarity observations, the Hierarchical Clustering Algorithm sort clusters hierarchical order.

These are just some of the algorithms. In the term of Accuracy and speed, Algorithm has its own advantages and disadvantages. AI has given a new face to tech in all fields to solve complex problems. Each algorithm can be selected on the need of data points.