What are ten AI al­gorithms worth knowing about?

Ar­ti­fi­cial in­tel­li­gence allows machines to learn from data, recognise patterns and make decisions on their own. AI al­gorithms form the backbone of all AI-driven systems and software.

What are AI al­gorithms?

An AI algorithm is a set of rules or in­struc­tions that allows machines to perform tasks typically requiring human in­tel­li­gence. These al­gorithms analyse data, find patterns and make pre­dic­tions, and range from simple decision trees to complex neural networks. AI al­gorithms determine how a machine processes data, which patterns it re­cog­nises and how it responds. Used in everything from online shopping to voice as­sist­ants and medical diagnosis, the key to using AI ef­fect­ively is choosing the right algorithm. Equally, the dif­fer­ences between AI al­gorithms lie in how they work, how they learn and the types of problems they are best suited to solving.

What are 10 AI al­gorithms worth knowing about?

AI al­gorithms lie at the core of all AI-driven systems. Here are ten AI al­gorithms worth knowing about. You’ll also learn how each one works and see some real-world examples of how they’re used.

Linear re­gres­sion

Linear re­gres­sion is one of the core al­gorithms in machine learning. It tries to find a linear re­la­tion­ship between a dependent variable (e.g., property price) and one or more in­de­pend­ent variables (e.g., location, size, age of the property). To do this, the algorithm creates a line (for one in­de­pend­ent variable) or a hy­per­plane (for multiple variables) that closely fits the data points. The goal is to minimise the dif­fer­ence between the predicted and actual values, also known as the error. To achieve this, math­em­at­ic­al methods like the least squares method are used.

Linear re­gres­sion is used in financial analysis to help forecast stock prices or revenue, and in marketing to analyse how different factors affect sales figures. Because it’s easy to un­der­stand, linear re­gres­sion is ideal for beginners in data analysis. Its sim­pli­city, however, doesn’t stop it from de­liv­er­ing clear and reliable results across a wide range of fields.

Real-world example:

An estate agency wants to estimate how much a property is worth. An AI algorithm analyses his­tor­ic­al property data, such as size, age and location and uses this in­form­a­tion to create a re­gres­sion line that predicts the price. From there, the algorithm helps the company quickly provide price estimates for new prop­er­ties.

Logistic re­gres­sion

Logistic re­gres­sion is used for clas­si­fic­a­tion problems, where the goal is to cat­egor­ise objects or events into specific groups. Unlike linear re­gres­sion, it doesn’t predict a specific value. Instead, it cal­cu­lates the prob­ab­il­ity that an event will occur. To do this, the algorithm computes a linear com­bin­a­tion of the input variables and then applies a sigmoid function, trans­form­ing the result into a value between 0 and 1. This value is in­ter­preted as a prob­ab­il­ity, with values above a certain threshold being assigned to a specific category.

Real-world example:

An email provider wants to auto­mat­ic­ally classify incoming messages as spam or not. The algorithm analyses features like the sender’s address, keywords and the number of external links to calculate the prob­ab­il­ity that an email is spam. If, based on this cal­cu­la­tion, the prob­ab­il­ity exceeds 50%, the system marks the email as spam.

Decision trees

Decision trees are a type of algorithm that, as the name suggests, represent decisions in a tree-like structure. Each node in the tree cor­res­ponds to a question or condition, and each branch leads to another condition or an outcome (the leaf). At each decision point, the AI algorithm chooses the feature that best splits the data into different cat­egor­ies. It uses criteria like in­form­a­tion gain or the Gini index to determine the most effective question to ask at each node. The result is a model that makes pre­dic­tions based on the values of these features.

Decision trees are easy to represent visually and un­der­stand­ing them is equally straight­for­ward. They require re­l­at­ively little data pre­pro­cessing and can be used for both clas­si­fic­a­tion and numerical pre­dic­tions. They can also be combined in random forest models to improve pre­dic­tion accuracy.

Real-world example:

In health­care, decision trees can be used to assess a patient’s risk of de­vel­op­ing heart disease. The tree starts with a question like “Is their blood pressure high?”. Depending on the answer, it moves on to other questions such as, “Does the patient smoke?” or “What’s their cho­les­ter­ol level?”. The tree even­tu­ally reaches a leaf that clas­si­fies the patient as either “high risk” or “low risk”.

Random Forest

Random Forest builds on decision trees by combining many of them to improve accuracy. The algorithm creates a large number of decision treesand each one is trained on random subsets of the training data and features. Each tree makes an in­de­pend­ent pre­dic­tion, and the final result is de­term­ined by a majority vote for clas­si­fic­a­tion or averaging for re­gres­sion. By combining multiple trees, errors from in­di­vidu­al trees are balanced out, making the overall pre­dic­tion both more accurate and more stable. Random Forest is flexible, can handle large datasets, and is less likely to overfit (become too spe­cial­ised to the training data) compared to a single decision tree.

Real-world example:

Random Forest is often used in e-commerce to predict whether a customer will buy a specific product. Each tree in the Random Forest evaluates the purchase like­li­hood based on different factors such as age, past purchases, how often they visit the site and location. The pre­dic­tions from all trees are then combined, and the product is re­com­men­ded to customers if the majority of trees agree it is likely to be relevant for them.

k-Nearest Neighbors (kNN)

kNN is a simple yet highly intuitive AI algorithm that makes pre­dic­tions based on sim­il­ar­it­ies between data points. When new data is entered, the algorithm cal­cu­lates how far it is from all the existing data points, usually using metrics like Euclidean distance. It then selects the k nearest neigh­bours, meaning the k data points most like the new input:

• For clas­si­fic­a­tion, the new data is placed in the category that most of the neigh­bours belong to.
• For re­gres­sion, the pre­dic­tion is made by averaging the values of the neigh­bours.

kNN is simple to implement but requires enough rep­res­ent­at­ive training data to make accurate pre­dic­tions. Proper pre­pro­cessing is also essential, par­tic­u­larly when scaling features. Despite its sim­pli­city, kNN can deliver strong results across a wide range of areas.

Real-world example:

A streaming service wants to predict which films a user might like. A kNN algorithm looks at the behaviour of other users with similar viewing habits – the ‘nearest neigh­bours’ – and re­com­mends films that those users have rated highly. Choosing the right value for ‘k’ is crucial: too small a value can lead to unstable pre­dic­tions, while too large a value can reduce the influence of certain pref­er­ences.

Support Vector Machines (SVMs)

Support Vector Machines are al­gorithms designed to separate data points from different classes as ef­fect­ively as possible. The algorithm searches for a dividing line or hy­per­plane that maximises the distance between the classes. The data points closest to this line are called support vectors: they play a key role in de­term­in­ing its position. SVMs can also handle non-linear clas­si­fic­a­tion problems, using kernel functions to transform the data into a higher-di­men­sion­al space where linear sep­ar­a­tion is possible. SVMs perform par­tic­u­larly well when the data is well-separated and generally deliver highly accurate results. One downside, however, is that pro­cessing very large datasets can be resource-intensive.

Real-world example:

An online banking service wants to use an SVM to dis­tin­guish between fraud­u­lent and le­git­im­ate trans­ac­tions. The SVM analyses elements such as trans­ac­tion amount, time, location and past user behaviour, and searches for a dividing line that clearly separates fraud­u­lent trans­ac­tions from le­git­im­ate ones. The support vectors, the trans­ac­tions closest to the dividing line, are critical in de­term­in­ing how future trans­ac­tions are clas­si­fied.

Naive Bayes

Naive Bayes is a prob­ab­il­ist­ic clas­si­fic­a­tion algorithm based on Bayes’ theorem. It assumes that all features of a data point are in­de­pend­ent of one another. The algorithm cal­cu­lates the prob­ab­il­ity that a data point belongs to a par­tic­u­lar class based on observed features. The data point is then assigned to the class with the highest prob­ab­il­ity. Naive Bayes is fast, efficient and robust, even with small training datasets. Despite assuming that features are in­de­pend­ent, it delivers reliable results across a range of text clas­si­fic­a­tion tasks.

Real-world example:

Naïve Bayes is commonly used in online shops to auto­mat­ic­ally classify customer reviews as either ‘positive,’ ‘neutral,’ or ‘negative.’ To do so, the algorithm looks at things like how often certain words (e.g., ‘good,’ ‘bad,’ ‘re­com­men­ded’) appear in the reviews. Based on this in­form­a­tion, Naive Bayes cal­cu­lates the prob­ab­il­ity that a review belongs to each of the cat­egor­ies and assigns it to the one with the highest prob­ab­il­ity.

K-Means

K-Means is a clus­ter­ing algorithm that divides data into groups, known as clusters, with similar char­ac­ter­ist­ics. The algorithm starts by randomly selecting a pre-set number of cluster centres, k. Each data point is then assigned to the nearest cluster centre. Af­ter­wards, the cluster centres are re­cal­cu­lated based on the assigned points. This process is repeated it­er­at­ively until the clusters stabilise. The choice of k (the number of clusters) is crucial to the quality of the results: too few clusters can obscure patterns, while too many can create overly specific groups that are not mean­ing­ful.

Real-world example:

In marketing, K-Means is used to group customers based on their pur­chas­ing behaviour. Customers with similar shopping habits are placed in the same clusters, allowing busi­nesses to create targeted offers and re­com­mend­a­tions. K-Means is also used in image pro­cessing, anomaly detection and to identify patterns in un­struc­tured data. It’s also es­pe­cially useful for finding hidden trends in large datasets.

Back­propaga­tion

Back­propaga­tion is used to train neural networks and forms the found­a­tion for many deep learning models. This algorithm works by adjusting the con­nec­tions between neurons based on the dif­fer­ence between the network’s predicted output and the actual result. The error is sent backwards through the layers, helping the network learn from its mistakes and improve its pre­dic­tions over time. Back­propaga­tion is often combined with gradient descent to adjust the network’s para­met­ers (or ‘weights’) and reduce this error.

Real-world example:

In speech re­cog­ni­tion, a system converts spoken words into text. Initially, the system makes pre­dic­tions that are often wrong. Back­propaga­tion helps it improve by cal­cu­lat­ing the dif­fer­ence between the predicted text and the actual words, then sending this error backward through the network. Over time, the network adjusts its con­nec­tions, learning from its mistakes and getting better at un­der­stand­ing specific pro­nun­ci­ations.

Re­in­force­ment learning

Re­in­force­ment learning involves AI learning to make decisions through trial and error. The algorithm interacts with its en­vir­on­ment, receiving rewards for desired behaviour and penalties for unwanted behaviour. The goal is for the AI to develop a strategy, or policy, that maximises long-term rewards. Unlike su­per­vised learning, the AI doesn’t need to know the correct answer in advance for every situation. Instead, it learns by itself based on the con­sequences of its actions. This approach shows how AI can solve complex problems on its own by learning from ex­per­i­ence, con­sid­er­ing long-term con­sequences and de­vel­op­ing strategies without explicit pro­gram­ming.

Real-world example:

In robotics, re­in­force­ment learning is used to train robots to navigate an obstacle course in­de­pend­ently. At first, the robot stumbles or falls over fre­quently, but through repeated attempts, it learns which movements lead to success and adjusts its behaviour ac­cord­ingly. After many training runs, the robot develops a strategy that allows it to complete the course quickly and ac­cur­ately.