What Are Support Vector Machine (SVM) Algorithms?

Key takeaways

• A support vector machine (SVM) algorithm is a supervised machine learning algorithm used for classification and regression tasks.

• You can use SVM machine learning for anomaly detection, text classification, handwriting recognition, prediction tasks, and gene classification.

• SVM works by locating the data points on the edges of a hyperplane (support vectors) that allow for the widest gap, which yields the optimal hyperplane.

You can use SVM algorithms to classify data points and determine the best way to separate data into binary classes. Continue reading to explore how support vector machine algorithms work, what you can use SVMs for, and careers that involve working with SVM algorithms.

To continue your learning, consider enrolling in the Machine Learning Specialization. In as little as two months, you’ll have the opportunity to build skills in applied machine learning, model evaluation, predictive modeling, and more.

What is an SVM?

An SVM algorithm, or support vector machine algorithm, is a supervised machine learning algorithm you can use for classifying data points and determining the best way to separate data into binary classes. You can use SVM algorithms in artificial intelligence and machine learning for applications, including speech and image recognition, email classification, and natural language processing.

SVM machine learning: How does it work?

The SVM algorithm works by locating the support vectors, which are the data points on the edges of a hyperplane. These points are the hardest to categorize because of their distance from the group’s center. They allow you to perform regressions and make sense of the relationships within data sets.

When you plot data on a graph, an SVM algorithm will determine the optimal hyperplane to separate data points into classes. It helps you categorize data points and understand how they relate to one another. While you may find infinite possibilities for separating data, a support vector machine algorithm can help you find the optimal hyperplane or the separation point that allows for the widest space gap between points.

Not all data can be neatly separated in two-dimensional space, so an SVM algorithm can plot the data in a higher-dimensional space to find its desired hyperplane. Some other algorithms run into difficulties, such as overfitting (training the machine learning program to be too specialized in one data set). An SVM algorithm can bypass this problem because it doesn’t need to directly engage with the data in the higher-dimensional space. This SVM ability uses kernel functions and is sometimes called a kernel trick.

What is a hyperplane?

In machine learning, a hyperplane is a flat subspace with one dimension less than its surrounding space (n-1 in an n-dimensional space). It serves as a decision boundary that divides data points into separate categories or regions.

What is SVM used for?

You can use SVM algorithms for classification and regression tasks, with practical applications in signal processing, natural language processing, speech and image recognition, handwriting recognition, email classification, and more. The following provides a closer look at some practical uses of support vector machine algorithms.

• Anomaly detection: You can use an SVM algorithm to set up a binary classification between “normal” returns and returns that would signify an anomaly in the data to easily distinguish data that falls outside expected results. The financial industry, for example, uses anomaly detection to detect fraudulent activity.

• Cancer detection: Medical researchers are using SVM algorithms to classify cancer-related genomic data to discover new biomarkers in patients, identify better drug candidates, and compare data to better analyze the genetic factors that fuel cancer.

• Assessing soil damage after earthquakes: Natural disasters like earthquakes can liquefy the soil, posing a danger to surrounding architecture. You can use an SVM algorithm to determine the state of the soil using samples taken from the site and analyzed via penetration tests.

• Understanding handwriting and classifying text: A support vector machine algorithm can recognize handwriting, a use case leveraged by the postal service for automatically reading addresses, by scoring data against its training material to determine how likely it is to fit into one category or another. It can also sort junk mail from important mail in your email inbox by analyzing word patterns and other linguistic features.

• Predicting common diseases: You can use SVM algorithms to analyze data from individuals with common conditions, such as diabetes or prediabetes, and categorize patients by their likelihood of developing these conditions based on biomarkers and other factors.

Who uses SVM algorithms?

SVM algorithms help scientists and machine learning specialists in various applications, from cancer research to speech recognition. When pursuing a career involving support vector machine algorithms, you may work on a range of projects across various industries.

Continue reading to explore more about three career options: machine learning engineer, data scientist, and image processing scientist, in which you may have the opportunity to work with SVMs. Note that the salary numbers represent the median total pay, which includes base salary and additional compensation, such as profit-sharing, commissions, bonuses, or other benefits.

Machine learning engineer

Median annual salary in the US: $165,000 [1]

Job outlook (projected growth from 2025 to 2035): 10 percent [2]

Education requirements: To start working as a machine learning engineer, you typically need a bachelor’s degree in information technology or a related field. For some roles, you may need to earn a more advanced degree, such as a master’s in computer science, machine learning, or a related field.

As a machine learning engineer, you will solve your organization’s problems using artificial intelligence and machine learning. You may design and develop new machine learning algorithms or platforms, troubleshoot problems in existing programs, and scale machine learning architecture to support growth. In this role, you might also need to manage communication between your client or company and your team to ensure you deliver the best results for your client.

Data scientist

Median annual salary in the US: $158,000 [3]

Job outlook (projected growth from 2023 to 2033): 35 percent [4]

Education requirements: To become a data scientist, you will likely need to earn a bachelor’s degree in mathematics, statistics, computer science, or a related field. For some positions, you may need a more advanced degree, such as a master’s.

As a data scientist, you will use data to get information for your company or organization. In this role, you might have the opportunity to work in various industries, including health care, manufacturing, retail, and more. While the specific projects you work on will vary, your responsibilities will include gathering data from various sources, developing AI and machine learning models to help you interact with the data, testing your models, and ultimately creating reports that visualize the insights you’ve gathered.

Image processing scientist

Median annual salary in the US: $105,000 [5]

Job outlook (projected growth from 2025 to 2035): 22 percent [6]

Education requirements: You may be able to start working as an image processing scientist with a bachelor’s degree, although some positions will require a master’s degree or a doctorate. Typical areas of study include electrical engineering, computer science, and similar fields.

As an image processing scientist, you will conduct research related to various forms of computer vision, such as medical imaging or optical visualization. In this role, you may develop AI or machine learning tools to improve image analysis. Potential projects you could work on in this career include target detection, enabling augmented reality or virtual reality, government-sponsored research, or researching new algorithms.

How to start a career with machine learning

Before beginning a career in machine learning, you’ll need to gain the skills, education, and experience you need to succeed in the field. Consider the following steps to becoming a machine learning engineer:

• Develop your skills: In this role, you’ll need to understand basic programming skills and advanced math, such as linear algebra, calculus, probability and statistics, and differential equations. You’ll also need to understand concepts like supervised versus unsupervised learning, deep learning, reinforcement learning, and natural language processing.

• Earn a degree: In most cases, you must earn at least a bachelor’s degree in computer science or a related information technology field to become a machine learning engineer. While this may be enough to start gaining real-world experience, you can consider earning a more advanced degree to qualify for a greater range of available jobs. You can also consider completing a boot camp or similar course to improve skills, close your education gaps, or specialize in the field.

• Work on real-world projects: The last piece of the puzzle is gaining experience in the field. Three ways to gain experience without a machine learning job are entry-level roles, internships, or projects developed at coding boot camps. Many machine learning engineers start in data science or other computer-related engineering fields before transitioning to machine learning.

Read more: What Are the Differences Between Machine Learning and AI?

Learn more with free resources on Coursera

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• Read our Career Chat Issue: 3 Machine Learning Projects to Build Your Skills and Portfolio

• Watch on YouTube: SVM Machine Learning │ MNIST Data Set Classification Tutorial

• Create a learning path: Machine Learning Roadmap: Beginner to Expert

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