12 Best Machine Learning Algorithms Data Scientists Should Know in 2024

Machine learning (ML) algorithms are the bedrock of some of the biggest apps in the world. Most popular apps and tools, from Google Search to ChatGPT and Siri, use them to deliver services to end users.

With the global machine learning market valued at $38.11 billion in 2022 and expected to reach $771.38 billion by 2032, more and more services will become AI-driven in the future.

But what are the best algorithms for budding data scientists and AI enthusiasts to learn about today?

To help answer this question, Techopedia has compiled a list of the top machine learning algorithms that AI enthusiasts should know, including a cheat sheet of the most widely used supervised and unsupervised machine learning algorithms.

We also reached out to Afraz Jaffri, Director Analyst at Gartner, who gave his insights and predictions for the future of ML development.

Whether you’re actively looking for a data scientist role or working on your own projects, knowing your way around these concepts can be incredibly useful.

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Key Takeaways

  • There are four main types of machine learning algorithms: supervised, semi-supervised, unsupervised, and reinforcement.
  • Different algorithms can be used to perform different tasks, such as data classification or regression.
  • Among the most recent ML techniques, Afraz Jaffri highlighted joint embedding predictive architecture (JEPA), graph neural networks (GNNs), neuro-symbolic AI, and quantum ML.
  • Knowing your way around the top ML algorithms can help enrich your understanding of how AI-driven applications like ChatGPT work under the hood.
  • Want to know how machine learning algorithms work? Use Techopedia’s cheat sheet and ML algorithms list as an introduction to some of the top machine learning algorithms.

12 Top Machine Learning Algorithms to Know in 2024

Machine Learning Algorithms Cheat Sheet: Top ML Algorithms Explained

Algorithm  What It Does  Use Cases  Algorithm Type 
Linear Regression  A linear regression algorithm predicts the relationship between two variables by assuming a linear relationship between a dependent variable or one or more independent variables. Often used to resolve binary classification problems and predictive analysis.

Real-world use cases include predicting the stock market and forecasting future sales trends.

Supervised learning algorithm
Logistic Regression  A logistic regression algorithm analyzes the relationship between multiple independent variables and classifies data into one of two binary categories: 0 or 1. Binary classification tasks and predicting the probability of a certain outcome.

Practical use cases include image recognition, fraud detection, credit scoring, and predictive diagnosis.

Supervised learning algorithm
Decision Tree  A decision tree algorithm consists of a hierarchical tree structure and is used to classify data/predict an outcome. Used in predictive modeling, regression, and classification tasks.

Often used to predict customer behavior and price movements and to diagnose certain conditions.

Supervised learning algorithm
Support Vector Machine (SVM)  An SVM algorithm is designed to identify a hyperplane or decision boundary to separate data points belonging to different classes. Used to resolve binary classification, regression, and outlier detection problems.

Typical use cases include text and image classification, spam filtering, facial recognition, and anomaly detection tasks.

Supervised learning algorithm
Random Forest  A random forest algorithm that combines the output of multiple decision trees to return a single, more accurate result. Used to solve classification and regression problems.

Applicable in risk monitoring, fraud detection, pricing, and recommendation engines.

Supervised learning algorithm
K-Nearest Neighbor (KNN) A KNN calculates the distance between data points to find the nearest neighbors to a given data point before classifying it based on this information. Used for classification and regression problems.

Developers can use KNN for pattern recognition, data mining, and intrusion detection.

Supervised learning algorithm
K-Means  A k-means algorithm groups data points together in clusters due to certain similarities. Used for predictive modeling.

Often used for tasks like market customer segmentation and fraud detection.

Unsupervised learning algorithm
Gradient Boosting  A gradient boosting algorithm is used to combine the predictions of multiple weak learning models iteratively, optimizing weights based on the previous model’s errors. Used for classification and regression tasks.

Best for processing large, complex datasets.

Supervised learning algorithm
Naive Bayes  A naive bayes classifier is a model designed to predict the probability of an instance belonging to a class based on the presence of certain features/values. Used for classification tasks.

Real-world use cases include text classification and spam filtering.

Supervised learning algorithm
XG Boost  An XG boost algorithm combines predictions from multiple models (or decision trees) to make more accurate predictions. Used for classification and prediction tasks.

This includes text classification and sentiment analysis.

Supervised learning algorithm
Stochastic Gradient Descent  A stochastic gradient descent algorithm is a technique that takes a random sample of data, calculates the gradient, and updates the model parameters. Commonly used to help build machine learning models and neural networks. Unsupervised learning algorithm
Apriori  An apriori algorithm is used to calculate association rules between objects, i.e., how two or more objects relate to each other to find itemsets in a database. Used to identify frequent or repetitive itemsets in a dataset.

It can be used by an organization to mine product sets.

Unsupervised learning algorithm

1. Linear Regression

One of the most common types of ML algorithms is a linear regression algorithm. A linear regression algorithm is a type of supervised learning algorithm that’s designed to predict the relationship between two variables by assuming a linear relationship between a dependent variable or one or more independent variables.

This approach means that the model can predict the value of the dependent variable and forecast future events. Developers can use this model to resolve binary classification problems.

2. Logistic Regression

Logistic regression is a supervised learning algorithm that predicts the probability of an outcome by analyzing the relationship between a set of independent variables.

A logistic regression algorithm analyzes the relationship between multiple independent variables and classifies them into binary classes 0 and 1. A value of less than 0.5 denotes 0, and more than 0 denotes 1. These values can stand for literally 0 and 1, yes or no, or true or false.

This algorithm is generally used to predict the probability that an instance belongs to a given category or group. For example, it could process an email and sort it into a spam category. It can also be used in other tasks, including image recognition, fraud detection, and credit scoring.

3. Decision Tree

A decision tree algorithm is a non-parametric supervised learning algorithm that uses a decision tree to classify data and predict outcomes. The tree is a hierarchical structure consisting of a root node, branches (decision nodes), and lead nodes, which function like a flow chart.

The root node acts as the base of the tree (the tree is depicted upside down with the root node at the top). To begin, the root node starts dividing input data using if-then statements, i.e., if a certain condition is true of a value, then it will be sorted into a particular node.

Root nodes are connected to decision nodes via branches. Once there are no more decisions to make, the data resides in a leaf node.

Developers typically use decision trees to complete classification and regression tasks. These algorithms provide transparency over why data was placed into a particular category.

Decision Tree Algorithm

4. Support Vector Machine (SVM)

Support Vector Machine or SVM is a supervised learning algorithm that’s used to solve classification, regression, and predictive modeling tasks.

The SVM algorithm is used to identify an optimal hyperplane or decision boundary to separate data points belonging to different classes. The algorithm aims to separate these the closest data points in each class via the largest possible margin.

Developers can use SVM to make more accurate classification predictions. As a result, it’s widely used in areas such as text classification, image classification, spam filtering, facial recognition, and anomaly detection tasks.

5. Random Forest

A random forest algorithm combines the output of multiple decision trees to produce a single result. As part of this approach, each tree is trained on a random sample of training data as part of a process known as bagging.

Each tree produces its own prediction, and then the average or majority prediction is then used to make a more accurate prediction. Random forest algorithms are generally used to resolve classification and regression problems.

6. K-Nearest Neighbor (KNN)

K-nearest neighbor is a supervised learning algorithm that’s used in classification and predictive modeling.

KNN classifies data points based on how close they are to their neighbors. The idea is that similar data points will be located close together. It’s worth noting that the value of K is the numerical value that determines the number of data point neighbors that will be used to classify an output.

At a high level, KNN can be used to resolve classification and regression problems. More specifically, it can be used to perform pattern recognition, data mining, and intrusion detection tasks.

K-Nearest Neighbor (KNN) Algorithm

7. K-Means

K-means is an unsupervised algorithm designed to group similar data points together to solve pattern recognition and clustering problems.

This algorithm calculates the distance between the data point and a centroid to assign it to a cluster. Each centroid is allocated to a cluster and is kept as small as possible. The end goal is to cluster data points with similar features together while minimizing the sum of distances between each point and the cluster centroid.

K-means can be used for predictive modeling and to complete complex tasks such as customer segmentation and fraud detection.

8. Gradient Boosting

Gradient boosting algorithms are an ensemble training approach where multiple weak learning models are combined together. Under this approach, the predictions of each model are taken and used to optimize weights based on errors of the previous models.

Doing this increases the overall accuracy of the model’s output to build more powerful predictive models. Developers can also use gradient boosting to resolve regression and classification problems.

9. Naive Bayes

A naive bayes classifier is a machine learning algorithm that uses Bayes’ theorem to classify objects. It’s designed to predict the probability of an instance belonging to a class based on the presence of certain features/values.

Bayes’ theorem assumes that a feature in a given class is unrelated to other features or factors. Instead, each feature or factor of a class independently contributes to the probability of a class fitting into a certain category.

This approach makes naive Bayes a good option for building predictive models. It can thus be used in a wide range of use cases including sentiment analysis and spam filtering.

10. XGBoost

XGBoost, or extreme gradient boosting, is a type of gradient-boosting algorithm that has a high level of accuracy in classification and predictive tasks.

An XGBoost algorithm is a scalable distributed-boosted decision tree that combines multiple decision trees in an attempt to increase the accuracy of predictions.

XGBoost is often used to solve regression and classification problems. Real-world applications include text classification and sentiment analysis.

11. Stochastic Gradient Descent

Stochastic gradient descent is a machine learning optimization model that searches for an objective function’s optimum value. It does this by searching for a random sample in training data, calculating the gradient, and then updating the model’s parameters.

Using stochastic gradient descent can result in random optimizations to the model but is often used to help train neural networks to help identify the best model parameters for performance and accuracy.

12. Apriori

Apriori, also known as frequent pattern mining, is an unsupervised learning algorithm that’s often used for predictive modeling and pattern recognition.

An Apriori algorithm identifies the most frequent items in a given dataset and can be used to identify association rules between multiple objects. This means that enterprises can use it to mine product sets and to generate association rules.

The Future of ML Algorithms: Exper’s Insights

New machine learning algorithms and techniques are developing all the time. In 2017, OpenAI released research highlighting reinforcement learning from human feedback (RLHF), which has since become a staple in the development of modern large language models (LLMS).

Gartner’s Director Analyst Afraz Jaffri highlighted some key areas where ML solutions will evolve in the future. He told Techopedia:

 “There are two main areas of focus for ML advancement – one is to be able to move away from very specific tasks such as prediction of numbers, text, images, video into more abstract representations of the world that can include and cater tour tasks that involve planning, reasoning, and evaluation with some level of inherent understanding of physical space and ‘common sense.’ This would be able to cater for different types of tasks in a single model.”  

The other area is to achieve more accurate levels of prediction for single tasks that are inherently complex and non-trivial, according to Jaffri.

“Examples include climate forecasting, drug discovery (protein folding) and quantum physics.”  

Other key areas highlighted by Jaffri include joint embedding predictive architecture (JEPA), which can predict complete structure from incomplete data, graph neural networks (GNNs) providing a richer representation of domains for predictive tasks in areas like climate and traffic prediction, and quantum ML, which combines the principles of quantum mechanics and ML to produce new, more accurate algorithms and processing techniques.

Joint Embedding Predictive Architecture (JEPA)

For building abstract world models, the work at FAIR has centered around the so-called JEPA architecture and its variants, which aim to use non-contrastive techniques to predict complete structures from incomplete data, according to Jaffri.

“This is then fused together at different levels of abstraction and hierarchy to create something similar to a planning and reasoning engine,” he said.

Reinforcement Learning

Deep reinforcement learning is also used for various scenarios that require multiple types of reasoning. Jaffri said:

“Recent work at DeepMind introduced the SIMA (Scalable Instructable Multiworld Agent) agent for being able to analyze an environment and then take actions to achieve an instructed goal.”

Graph Neural Networks

GNNs represent the world as nodes and edges or entities and relationships, which can provide a richer representation of a domain for predictive tasks, according to Jaffri.

He said:

“This has led to breakthroughs in climate and traffic prediction and even the transformer architecture on which Large Language Models are based derived from graph theory.”

Neuro-Symbolic AI

Neuro-symbolic AI aims to create domain models by merging machine learning techniques with formal rules-based systems to overcome the fragility and high data utilization of purely ML models, Jaffri explained.

“Such approaches are starting to bear fruit with the combination of LLMs with knowledge graphs for Retrieval Augmented Generation (RAG) GenAI use cases. There are many researches looking to combine these approaches in a tighter model.”

Quantum ML

Quantum ML is “an area of active research but with unknown impact,” according to Jaffri.

He told Techopedia:

“Quantum ML combines the principles of quantum mechanics and machine learning (ML) to develop new algorithms, hybrid methods and new processing techniques to analyze data faster, with more accuracy and efficiency than classical approaches alone.”

“This will require breakthroughs in quantum computing hardware before the algorithms can be applied,” Jaffri added. 

It’s also important to consider that we can expect to see ML become more accessible as a technology, particularly as AutoML solutions make it easier for business users to automate the development and deployment of their own ML models. 

The reality is that ML technologies are developing so fast that the barrier to entry to generating AI-driven insights is decreasing all the time.

The Bottom Line

No matter how much you know about AI, getting to grips with some of the basic underlying algorithms will help to improve your understanding of how these solutions are redefining data processing.

It’s important to note that there’s no one right machine learning algorithm – all the tools are designed to function in different use cases.

FAQs

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Tim Keary
Technology Specialist

Tim Keary is a freelance technology writer and reporter covering AI, cybersecurity, and enterprise technology. Before joining Techopedia full-time in 2023, his work appeared on VentureBeat, Forbes Advisor, and other notable technology platforms, where he covered the latest trends and innovations in technology. He holds a Master’s degree in History from the University of Kent, where he learned of the value of breaking complex topics down into simple concepts. Outside of writing and conducting interviews, Tim produces music and trains in Mixed Martial Arts (MMA).