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 logic, generate output. However, Artificial Intelligence Algorithms use both output and input to generate new data output after getting new inputs.

**An Introduction To Artificial Intelligence**

Artificial intelligence is a particular field of computer science that deals with integrating the ability to independently think and make decisions to machines. This is an amalgamation of computer science, data analytics, and pure mathematics.

The end goal with AI is to create systems and mechanisms that can assist humans with the motive of optimizing performance and opening up growth potentials that would otherwise be not achievable.

Since the growth of AI is reliant on gaining information from a wide variety of sources, the most important aspect is the ‘learning’ that the AI has to go through. It is here that we see the birth of machine learning which aims to empower machines to teach themselves and learn from a wide variety of input.

However, this learning process requires computing systems and algorithms that can support the entire journey to ensuring that the AI gains valuable knowledge from the variables around it.

These algorithms work in tandem with the computing software/hardware to create specific learning journey’s for the AI which enables it to perform at its optimal level while also helping its users to create information/database structures to enable consistent learning in the future.

**What’s An Algorithm?**

The dictionary definition for algorithm states that it is a simple “step-by-step procedure for calculations”; and that just might be the most perfect definition.

Algorithms are simply just mathematical instructions for calculation, data processing, and automated reasoning. This is a process as old as time. Since we’ve been mathematically figuring out almost everything and anything, with it now being more computing-centered than it was before.

When integrating algorithms within computing systems, you have the option to either input the exact requirements or have the computer chart out its own journey through the usage of machine learning (what AI does). Either way, the computer is at the center of this computing process, with the user providing it with the foundation based on which it can grow and learn from the variables around them.

**Artificial Intelligence Algorithms**

Artificial intelligence as we’ve understood above is a ‘technology superset’ that encompasses a wide variety of tools/resources. The most crucial of which is machine learning since it forms the foundation on which AI works.

Before we get into the types of artificial intelligence algorithms. It’s important for us to first discover the three major types/groups of algorithms that the entire system revolves around. Once we’ve understood the overarching groups, we can dive into the specific algorithms and how they’re used within the realm of AI.

**Reinforcement Learning**

The first group of algorithms we’re looking at is reinforcement learning which consists of a constant iteration that’s based on the “trial and error” system. Through this learning model, machines carry out jobs under certain conditions or in particular environments with a specific objective (called ‘reward’ in this case) in mind.

In this model, the computing system goes through multiple trial scenarios. To find the most optimum pathway that can then be used in the future for different scenarios.

Through this particular learning model, you can obtain not just results, but also discover patterns, correlations, paths, and conclusions based on previous experiences that have been generated by the machine itself.

In this model, algorithms such as dynamic programming, Q-Learning, and SARSA (state-action-reward-state-action) are in action to contribute to the overall learning process.

Deep Reinforcement Learning And Its Applications

**Supervised Learning**

This form of learning finds its basis in predictive models that utilize training data i.e. the computing system has a known set of data. It uses that data to achieve a certain output by going through an adjustment/training period which stops only when the adequate/required results are visible.

Here, the end goal is clear and a starting point is shared. After which it is on the computing system to train itself to achieve that particular goal in the most optimum manner possible.

You would generally find algorithms such as decision trees, naïve Bayes classifiers, Support Vector Machines, and regressions in supervised learning.

**Unsupervised Learning**

Unsupervised learning algorithms are similar to supervised learning algorithms with the only difference being their complete reliance on input data.

In other words, the algorithm here performs a self-training/learning process. Without the need for any form of external intervention. Instead, it just requires a set of input data after which it charts out its own learning journey.

**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 is 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. However, 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**

It was in 1948 that Alan Turing created a complex algorithm even though the computing systems would not be able to carry out such complex calculations until a couple of years later. However, he was able to create the foundation. This has now turned into a highly intricate system that utilizes several data points and learning journeys to configure systems and chart outgrowth paths in the world of data.

With AI, the focus is on solving complex problems in an attempt to create solutions. These could help us reach our potentials while also bringing about big-time change in the overall scheme of things.

While the technology might just be picking up pace. It has been able to showcase the potential it has to bring about this change which has posed it as a technology that deserves the utmost time and attention from anyone and everyone hoping to advance in this technology-centric digital world.

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.