What Are Support Vector Machine (SVM) Algorithms?

SVM algorithms, or support vector machine algorithms, are tools for artificial intelligence and machine learning to classify data points and determine the best way to separate data in binary classes. SVM algorithms are useful for many machine learning applications, like speech and image recognition, email classification, and natural language processing. 

Delve further into the article to explore how support vector machine algorithms work, how different industries use them, and options for careers working with SVM algorithms. 

What is an SVM?

An SVM algorithm, or a support vector machine, is a machine learning algorithm you can use to separate data into binary categories. When you plot data on a graph, an SVM algorithm will determine the optimal hyperplane to separate data points into classes. It assists in categorizing data points and understanding how they relate to one another. You may find an infinite number of possibilities for how to separate data, but a support vector machine algorithm helps you find the optimal hyperplane or the separation point that allows for the widest gap of space between points. 

The SVM algorithm works by locating the support vectors—the data points that exist on the edges of an optimal hyperplane. These points are the hardest to categorize because of their distance from the middle of the group. They allow you to perform regressions and make sense of the relationships within data sets. 

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 like overfitting (or training the machine learning program to be too specialized in one data set). An SVM algorithm can bypass those problems because it doesn’t need to engage with the data in the higher dimensional space directly. This SVM ability uses kernel functions and is sometimes called a kernel trick. 

What is an SVM used for?

SVM algorithms can be used for classification and regression, with practical applications in signal processing, natural language processing, speech and image recognition, handwriting recognition, email classification, and more. The following offers a closer look at some practical uses for supporting 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. 

• Cancer detection: Medical researchers are using SVM algorithms to classify cancer-related genomic data to discover new biomarkers in patients, understand who is a better candidate for drugs, and compare data to better analyze the genetic factors that fuel cancer. 

• Assessing damage to soil 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 through penetration tests. 

• Understanding handwriting: A support vector machine algorithm can understand and recognize handwriting by scoring data against its training material to determine how likely the new material will be in one category or another. It is the same way an SVM can sort junk mail from important mail in your email inbox. 

• Predicting common diseases: You can consider the data from individuals with common diseases like diabetes or prediabetes to categorize patients in regards to their likelihood of developing those diseases based on their biomarkers and other factors. 

Who uses SVM algorithms? 

SVM algorithms help scientists and machine learning specialists in various applications, from cancer research to recognizing speech.  When pursuing a career using support vector machine algorithms, you might work in several industries or projects. Three career titles to consider include machine learning engineer, data scientist, and image processing scientist. 

Machine learning engineer

Average annual salary in the US: $122,797 [1]

Job outlook (projected growth from 2023 to 2033): 17 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 create 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 the communication between your client or company and your team to ensure you deliver the best results for your client. 

Data scientist

Average annual salary in the US: $118,045 [3]

Job outlook (projected growth from 2023 to 2033): 36 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 exact 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

Average annual salary in the US: $92,747 [5]

Job outlook (projected growth from 2023 to 2033): 26 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, or a similar area of study. 

As an image processing scientist, you will conduct research related to various forms of computer vision, such as medical imaging or optical visualization. You may develop AI or machine learning tools to improve image analysis in this role. 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. Let’s take a closer look at each of these 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 will need to 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 brush up on skills, close your education gaps, or specialize in the field. 

• Work on real-world projects: The last piece of the puzzle is to gain experience working in the field. Three ways to gain experience in the field 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 fields of computer-related engineering before coming to machine learning.

Read more: Machine Learning vs. AI: Differences, Uses, and Benefits

Learn more with Coursera.

An SVM algorithm has many practical uses ranging from medical research to natural language processing. Learning more about these algorithms can open the door to various career opportunities in machine learning. To take the next step, consider taking courses like Build Decision Trees, SVMs, and Artificial Neural Networks offered by CertNexus on Coursera. This intermediate-level course takes approximately 21 hours to complete and is part of the CertNexus Certified Artificial Intelligence Practitioner Professional Certificate.