**Artificial Intelligence** has grown to have a significant impact on the world. With large amounts of data being generated by different applications and sources, machine learning systems can learn from the test data and perform intelligent tasks.

Artificial Intelligence is the field of computer science that deals with imparting decisive ability and thinking ability to machines. Artificial Intelligence is thus a blend of computer science, data analytics, and pure mathematics.

Machine learning has become an integral part of Artificial Intelligence, and it only deals with the first part, the process of learning from input data. Artificial Intelligence and its benefits have never ceased to amaze us.

The artificial intelligence market is expected to grow by leaps and bounds. The market is expected to grow at the rate of 39.4% by 2022-2028. The artificial intelligence allows the organisations to make better decisions and put better measures to increase the growth.

The artificial intelligence increases the accuracy and decision-making process of the company. It also helps in automation of tasks. Some of the examples of artificial intelligence would be self driving cars, visual assistants, face unlock, etc.

There are various types of artificial intelligence, such as reactive, limited memory, theory of mind, and self aware. The machine performs various AI algorithms in order to carry out the tasks. Algorithms are subsets of machine learning that automates the process of machine learning. It is these algorithms that tell the machine how to learn on its own.

There are algorithms artificial intelligence grouped into broadly three categories such as supervised learning, unsupervised learning and reinforcement learning.

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**Types of Artificial Intelligence Algorithms**

**Artificial intelligence algorithms can be broadly classified as :**

**1. Classification Algorithms**

Classification algorithms are part of supervised learning. These algorithms are used to divide the subjected variable into different classes and then predict the class for a given input. For example, classification algorithms can be used to classify emails as spam or not. In the classification algorithms artificial intelligence classifies a new category of observations based on the existing data which we can call as training data as well. The program learns from the dataset that is already given. Let’s discuss some of the commonly used classification algorithms.

** a) Naive Bayes**

Naive Bayes algorithm works on Bayes theorem and takes a probabilistic approach, unlike other classification algorithms. The algorithm has a set of prior probabilities for each class. Once data is fed, the algorithm updates these probabilities to form something known as posterior probability. This comes useful when you need to predict whether the input belongs to a given list of classes or not.

This probabilistic classifier predicts on the basis of probability. The Naive Bayes algorithm that is a probabilistic classifier is used in sentiment analysis, recommendation, spam filtering, etc. It is called as Naive Bayes because it assumes class conditional independence. The attribute value of a given class is independent of the values of other existing attributes.

** b) Decision Tree**

The decision tree algorithm is more of a flowchart like an algorithm where nodes represent the test on an input attribute and branches represent the outcome of the test.

It is a very simple kind of a probabilistic tree that enables to make decisions about some kind of process. This tool assumes a tree like model and its possible consequences.

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** c) Random Forest**

Random forest works like a group of trees. The input data set is subdivided and fed into different decision trees. The average of outputs from all decision trees is considered. Random forests offer a more accurate classifier as compared to Decision tree algorithm.

Existence of many decision trees is random forest algorithm in classification. In order to build uncorrelated forest trees it uses the features of bagging randomness while building individual trees. This allows the prediction to bemore accurate as compared to the individual tree.

Random forests is used in many industries such as healthcare, manufacturing, banking, retail, etc. One of the real-life applications of random forest would be to decide if an email is spam or not spam.

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** d) Support Vector Machines**

SVM is an algorithm that classifies data using a hyperplane, making sure that the distance between the hyperplane and support vectors is maximum.

It is a supervised learning algorithm that can be used for either classification and regression problems. One of the example of SVM is Face detection, classification of images, hand writing detection, text and hypertext categorization, etc.

** e) K Nearest **Neighbors

KNN algorithm uses a bunch of data points segregated into classes to predict the class of a new sample data point. It is called “lazy learning algorithm” as it is relatively short as compared to other algorithms.

Some of the applications of KNN is finance, medicine, such as bank customer profiling, credit rating, etc. There are various advantages to using KNN such as easy to implement and understand, also it is very simple and intuitive.

**2. Regression Algorithms**

Regression algorithms are a popular algorithm under supervised machine learning algorithms. Regression algorithms can predict the output values based on input data points fed in the learning system. The main application of regression algorithms includes predicting stock market price, predicting weather, etc. The regression algorithms also aids in predictign the output values based on the input features that are fed from the data. There are various types of regression such as linear regression, polynomial regression, etc. The most common algorithms under this section are

** a) Linear regression**

It is used to measure genuine qualities by considering the consistent variables. It is the simplest of all regression algorithms but can be implemented only in cases of linear relationship or a linearly separable problem. The algorithm draws a straight line between data points called the best-fit line or regression line and is used to predict new values.

One of the common examples of linear regression would be medical practice wherein the practioners understand the relationship between the sugar intake and high blood sugar levels.

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** b) Lasso Regression**

Lasso regression algorithm works by obtaining the subset of predictors that minimizes prediction error for a response variable. This is achieved by imposing a constraint on data points and allowing some of them to shrink to zero value.

The lasso regression is used to obtain the subset of predictors that helps in minimisng the error in prediction. Lasso puts a constraint on the model parameters that make the regression coefficients shrink to zero.

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** c) Logistic Regression**

Logistic regression is mainly used for binary classification. This method allows you to analyze a set of variables and predict a categorical outcome. Its primary applications include predicting customer lifetime value, house values, etc.

There are multiple real-life applications of logistic regression such as banking. A credit card company can know if the transaction amount and credit score will lead to fraudulent transaction or not.

** d) Multivariate Regression**

This algorithm has to be used when there is more than one predictor variable. This algorithm is extensively used in retail sector product recommendation engines, where customers preferred products will depend on multiple factors like brand, quality, price, review etc.

The multivariate regression helps in finding the relationship between multiple variables. Also in finding the correlation between dependent and independent variables.

** e) Multiple Regression Algorithm**

Multiple Regression Algorithm uses a combination of linear regression and non-linear regression algorithms taking multiple explanatory variables as inputs. The main applications include social science research, insurance claim genuineness, behavioural analysis, etc.

**3. Clustering Algorithms**

Clustering is the process of segregating and organizing the data points into groups based on similarities within members of the group. This is part of unsupervised learning. The main aim is to group similar items. For example, it can arrange all transactions of fraudulent nature together based on some properties in the transaction. There are various advantages to using clustering algorithms. As they are simpler to implement, adaptable to new examples, scale to large data sets and more. Some of the example of clustering algorithms would be identifying fake news, marketing, spam filter, etc. Below are the most common clustering algorithms.

** a) K-Means Clustering**

It is the simplest unsupervised learning algorithm. The algorithm gathers similar data points together and then binds them together into a cluster. The clustering is done by calculating the centroid of the group of data points and then evaluating the distance of each data point from the centroid of the cluster. Based on the distance, the analyzed data point is then assigned to the closest cluster. ‘K’ in K-means stands for the number of clusters the data points are being grouped into.

There are various applications to K- means clustering from banking to cybersecurity, search engines, etc. It is an unsupervised learning because the points have no external classification to them.

K-means has various real-life applications such as sentiment analysis, spam detection, etc. It is used where the user has the unlabeled data. Unlabeled data is that type of data which does not have a category or groups.

** b) Fuzzy C-means Algorithm**

FCM algorithm works on probability. Each data point is considered to have a probability of belonging to another cluster. Data points don’t have an absolute membership over a particular cluster, and this is why the algorithm is called fuzzy.

Fuzzy C- Means is a clustering technique wherein the data set gets grouped into N clusters where each data point in the dataset belongs to every cluster in one way or the other.

** c) Expectation-Maximisation (EM) Algorithm**

It is based on Gaussian distribution we learned in statistics. Data is pictured into a Gaussian distribution model to solve the problem. After assigning a probability, a point sample is calculated based on expectation and maximization equations.

The Expectation-Maximisation (EM) algorithm is used in those places where there is a need to find a local maximum likelihood parameters of a statistical model. It is also used in the places wherein the equations cannot be solved directly.

** d) Hierarchical Clustering Algorithm**

These algorithms sort clusters hierarchical order after learning the data points and making similarity observations. It can be of two types

- Divisive clustering, for a top-down approach
- Agglomerative clustering, for a bottom-up approach

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**Let’s wind up and conclude**

AI has startled the world multiple times and has a lot of applications in the real world to solve its complex problems. We hope this article has shed some light on the various Artificial Intelligence algorithms and their broad classifications. Algorithms are chosen based on the need and the nature of the data points we have.

Algorithms have their advantages and disadvantages in terms of accuracy, performance and processing time. These are just a few algorithms. If you are keen on learning more, check out upGrad & IIIT-B’s **Executive PG Programme in Machine Learning & AI.**