Top list of Artificial Intelligence Algorithms transforming world

AI can be said as the intelligence displayed by machines based on which the devices take actions and maximizes the chances of success of some goal. AI techniques have gained a great revival in the recent years due to advances in theoretical understanding, big data and technology. AI is helping people solve many challenging problems in computer science. Here is the Top list of Artificial Intelligence Algorithms transforming world.

Top list of Artificial Intelligence algorithms that are transforming worldThe term artificial intelligence is applied when a machine imitates the cognitive functions that are associated with human minds. Whether it is learning or problem solving, the scope of AI is huge.

The main objective of AI is to create a technology that allows machines to function in a smart way. Some of the traits that experts expect intelligent systems to display include – problem solving, reasoning, knowledge representation, default reasoning, planning, learning, natural language processing, perception, social intelligence, manipulation, creativity and general intelligence.

Artificial intelligence is changing our lives by helping us discover and focusing on subject matters that make human capital good. However, it becomes complicated as there are multiple levels of algorithms used in artificial intelligence. Algorithms have the ability to solve a particular problem and its value in artificial intelligence depends on their use.

Algorithms are a set of repetitive steps that are guided with simple rules to crunch a complex problem. The algorithms are given a data and they come up with a suitable solution based on the data. If the programmers are not satisfied with the answer, they give algorithms more data to fine-tune the answer.

AI Algorithms are used for data processing, calculation, testing and automated reasoning. To make computers do anything, a program must be written. The computer executes the steps to accomplish the end goal. The only thing is that you have to tell the computer “what you want to do exactly”.

Algorithms are integrated into the computers and can make the system smarter. However, without adding common sense – they still produce peculiar results. It is expected that AI algorithms will replace 25% of the jobs across the world in next few years. The market for AI technology is flourishing and there are numerous startups that are acquiring them. In a survey, it was found that more than 38% of the enterprises are using AI technology.

There are different types of algorithms that are used in artificial intelligence. This guide is meant to provide the Artificial Intelligence Algorithms transforming world. Before we go into detail, let’s know about different types of algorithms.

Supervised machine learning algorithm  

Supervised learning algorithm is where you have input and output variables. The main objective of this algorithm is to map functions in an effective way that the output can be predicted using input data. This process is known as supervised learning because the algorithm makes predictions based on data, which can be thought as a teacher is supervising the learning process.

Supervised learning can be classified into ‘classification’ and ‘regression’. The most common type of problems are built on supervised learning and includes – linear regression, random forecast and support vector machines.

Most of the people consider Artificial Intelligence as programmer assisted learning, which means the algorithms are predefined ahead of time. This machine learning is applied with supervised learning algorithm.

Unsupervised machine learning algorithm

Unsupervised learning is where you only have input variable and no output data. The main objective of this algorithm is to model the underlying structure in data to learn more about the data. Unsupervised learning algorithms are left on their own devices to discover and present structure of the data.

Unsupervised machine learning algorithms can be further classified into association and clustering problems. The most popular examples of unsupervised learning are – Apriori algorithm and k-means for clustering.

Besides this, there is an algorithm known as Reinforcement Machine Learning Algorithm that chooses the actions based on each data point and know how good the decision is. This algorithm can change it strategy from time to time to achieve the best reward.

Top list of Artificial Intelligence Algorithms
Artificial Intelligence Algorithms transforming world

Artificial Intelligence Algorithms has gained great popularity, as they can be learnt and improved without need of humans. Here are the top 7 algorithms that are used in artificial intelligence.

  1. Apriori Algorithm

Apriori algorithm generates association rules from given data set. According to association rule, if the item A occurs, then there is a possibility that item B will occur. This algorithm is in If_Then format, and the ratio is derived like if A occurs, then B will also occur. This algorithm is easily to implement and makes use of large set of properties. Some of the common uses of this algorithm in AI are – market based analysis, detecting adverse drug reactions, auto-complete applications etc.

This algorithm is used for association analysis on healthcare of patients and include – initial diagnosis, adverse effects of patients, characteristics of patients etc. many e-commerce platforms are using this algorithm to draw data insights on which products are likely to be purchased together. The libraries in AI implement this algorithm to find relation between different data sets.

  1. K-Means Clustering Algorithm

K-Means is a popularly used algorithm in AI for cluster analysis. This algorithm operates on the given data set through a number of clusters that are pre-defined. The K-clusters of this algorithm mean that the input data is partitioned among K-clusters. This algorithm is used by reputed search engines such as – Google, Yahoo and Bing to identify relevance rate of search results, reducing the computation rate for users.

In case of global clusters, K can produce tighter cluster than hierarchical clustering. For larger number of variables, K-means clustering can compute faster than hierarchical clustering.

  1. Logistic regression

Logistic regression is a machine learning algorithm that is used for classification of tasks. The name regression implies that the linear model will fit into feature space to predict the outcome of categorical dependent variables. The probabilities that describe the outcome are modeled as function of explanatory variables. This algorithm also helps to estimate the probability of falling into a specific level of variables based on predictor variables. Logical regression can be classified into – multi-nominal regression, binary logistic regression and ordinal logistic regression.

Logistic regression algorithm is less complex and easier to implement. It is applied in the field of epidemiology to identify the risk for disease, classify a set of sounds, weather forecasting, credit scoring systems and risk management.

  1. Naïve Bayes Classifier Algorithm

Naïve Bayes Classifier algorithm comes into play when it becomes difficult to classify a document, webpage, email or any lengthy text. The role of classifier function is to allocate element value from one of the categories. For example – ‘Spam filter’ is an important application of this algorithm that assigns label to all the emails as ‘spam’ and ‘not spam’.

Bayes classifier algorithm is meant for document classification and disease prediction. In short, it is a simple classification of words based on the probability Theorem of Bayes. If you have large data set, you can use this algorithm. Some of the popular uses of Naïve Bayes Classifier algorithm include – sentiment analysis, document categorization, email spam filtering, classifying new articles etc.

This algorithm performs well when the input data is categorical. It is easier to predict the class of test data set. Naïve Bayes Classifier algorithm gave good performance in various domains, even if it needs conditional independence assumption.

  1. Random forest algorithm

Random forest algorithm uses bagging approach to develop a bunch of decision trees. Based on this algorithm, the model is checked multiple times on a random set of data to achieve excellent prediction performance. The final prediction is derived by polling the results of decision trees. There are multiple implementations of this algorithm in AI. When the data is missing, it maintains accuracy and help scientists find missing data in no time.

Random forest algorithm can be implemented with minimal lines of code. It helps to estimate what type data is important in the classification and how they can be used. This is one of the best and versatile algorithms that are used for a variety of regression tasks. They are robust to noise and it is easy to determine which parameter to use to run the algorithm. Random forest algorithm can be grown in parallel and runs efficiently on large database.

Random forest algorithm is used in a variety of AI applications such as – voice recognition, predicting performance scores, automobile industry, banks and many more.

  1. Support vector machine learning algorithm

This machine learning algorithm is used for regression and classification problems. The data set teaches the algorithm about the classes so that the new data can be classified easily. The data is classified into different classes by finding a line that separates the training data into different classes. There are many hyperplanes that tries to maximize the distance between different classes.

Support vector machine learning algorithm offers the best classification performance on training data as well as renders more efficiency for future data classification. The best thing about this approach is that it neither makes string assumptions on data not over-fit the data.

SVM algorithm is widely used in stock forecasting to compare performance of stock in same sector. This comparison helps in managing the investment decisions based on the classifications made by this algorithm.

  1. Decision trees

Decision tree algorithm is a graphical representation of data that makes use of branching methodology. The outcomes of decision are exemplified based on certain conditions. The internal node of the branch represents test on the attribute, while the branch represents the outcome of the test. The decision is made after computing all the attributes.

The decision trees are categorized into classification trees and regression trees, based on the type of target variable. The target variable helps to decide the decision tree that is appropriate for a particular problem. This machine learning algorithm helps to make decision under uncertainty and help improve communication. The data scientists can capture the data based on the decision tree algorithm, and check how the operational nature of the model would change if a different decision was taken.

This algorithm is best suited for problems where the situation is represented by attribute value.  If the data has missing value, then the decision tree algorithm can be used – as it can handle the missing values by looking at the data in different columns. This type of algorithm is used in data exploration and help save data preparation time. It is not sensitive to missing values that will stop you from splitting the data for building decision tree.

The main drawback of this algorithm is the presentation difficulty, when it comes to large sized trees. Decision tree algorithm is highly popular in finance, remote sensing application, disease identification and many more.


Artificial Intelligence is the science of making intelligence systems using algorithms that span across several branches of computer science. Some of the popular branches include – genetic programming, predictive modelling, text mining, pattern recognition, data analytics and ontology. Artificial Intelligence algorithms are based on a number of mathematical methods such as – regression, classification, clustering, findings etc in the field of speech recognition, gaming, medical diagnostics, credit card fraud etc.

If you want to mater the algorithms used in AI, start right away. Develop a physical understanding of the process and apply the algorithms to see the fun.



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