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What is SVM? Machine Learning Algorithm Explained

6 minute read | June 10, 2020
Sakshi Gupta

Written by:
Sakshi Gupta

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There’s so much development, study, and confusion going on around Machine Learning Algorithms that we couldn’t miss out talking about it. Let’s start with what  Machine Learning is. To be precise, Machine learning is a subset of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Quite basic, right? Well, not really! Machine Learning algorithm like SVM opens a whole new world of possibilities and hence has many explored and unexplored dimensions. 

Machine Learning Algorithm: SVM (Support vector machine)

Today we’ll be talking about one such machine learning algorithm – SVM (support vector machine). 

To begin to understand this, we must know the areas where SVM is currently in use – 

  • Face detection
  • Classification of images
  • Text and hypertext categorization
  • Bioinformatics
  • Geo and Environmental Science
  • Handwriting recognition

 Yes, SVM has a role to play in all of that.

Now, that we know the applications, let’s dive right into the technicalities –

  1. Supervised – Here, we have the labeled/classified data to train the machines.
  1. Unsupervised – Here, we do not have labeled/classified data to train the machines.
  1. Reinforced – Here, we train the machines through rewards on the right decisions.

What is SVM?

It is a type of supervised machine learning algorithm. Here, Machine Learning models learn from the past input data and predict the output. Support vector machines are basically supervised learning models used for classification and regression analysis.

For example – Firstly, you train the machine to recognize what apples look like. After that, using that past data, it can always identify apples and give the output. 

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Why do we need Support Vector Machine?

SVM is a model that can predict unknown data. For example, if we have a pre-labeled data of apples and strawberries, we can easily train our model to identify apples and strawberries. So, whenever we give it new data – an unknown one – it can classify it under strawberries or apples. 

That’s SVM in play. It analyses the data and classifies it into one of the two categories based on the labeled data it already has. As per the previous example, it will sort the apples under the apple category and the strawberries under the strawberry category. 

But how does the prediction take place?

  1. Here, we have our Support Vector Machine where we take the labeled sample of data as seen in the first graph. 
  2. Further, we draw a line separating the two categories. This line is called the decision boundary. Herein one side of the decision boundary has apples and the other side has strawberries.
  3. Now when new data is taken as seen in the third graph, it automatically goes into the group it belongs to – the right or the left side of the decision boundary.
  4. And depending on which side of the line the unknown sample data goes, we can predict the unknown and classify it under the apple or strawberry category.  

Let’s get into the details
The aforementioned is a simple and clear example. 

The key here is –
To figure out if a new data point is a strawberry or an apple, we need to split our existing data in the best possible manner. We need to separate the two classes in a way that the decision boundary separates the two classes with maximum space between them. And the line that makes it possible best splits the data.

In the graph shown here, the blue line splits the two classes in the best possible manner. Why? Because (as shown by the green line) it creates the maximum distance between the two classes. The distance between sample points (the points closest to the dotted lines) and the (blue) line should be as far as possible. In technical terms, we can say that the distance between the support vectors and the hyperplane should be as far as possible. 

So, now you know exactly what support vectors are. They are the extreme points in data sets. And these extreme points are separated by the maximum distance via the hyperplane. The unknown data sets falling on the left or right side of the hyperplane are classified into their respective categories. The dotted lines shown in the graph hold significance too. 

The distance between the blue line and the dotted line on the right side is D+. Here, D+ is the shortest distance to the closest positive point. Whereas the distance between the blue line and dotted line on the left side is D-. Here, D- is the shortest distance to the closest negative point. The sum of d+ and d- then becomes the distance margin. And through the largest distance margin, an optimal hyperplane can be created and thus the classification of data can take place. 

Let’s move to a more complicated example. 

The previous example was a simple one because the data sets were nicely segregated in the first place. But what if we have a data set that looks like this –

Where one data set occurs with the other (there is red in one data set, and the red occurs again in the second data set with green).  There is no clear segregation. Well, in that case, how do we draw a hyperplane?
Here we’ll shift from a 1-dimensional view to the 2-dimensional view of the data.

Wait, how is that possible?

 Through kernel function. Kernel function takes the 1-d input and converts it into 2-d output.

Now that the data set is converted into 2-dimensional data, it becomes easy to draw a hyperplane and hence segregate the two classes. That is how a Support Vector Machine works. It comes with a whole gamut of advantages and disadvantages too. Let’s have a look. 

Advantages of Support Vector Machine (SVM)

1. Regularization capabilities: SVM possesses the L2 Regularization (Ridge regression) feature. L2 Regularization adds the squared magnitude of coefficient as penalty term to the loss function. It can generalize well which prevents it from over-fitting (modeling error which occurs when a function is closely fit in a limited set of data points).

2. Handles non-linear data efficiently: SVM efficiently handles non-linear data (where data items are not organized sequentially) through Kernel function.

3. Solves both Classification and Regression problems: SVM is used for classification problems while SVR (Support Vector Regression) is used for regression problems.

4. Stability: If there’s a slight change in the data, it does not affect the hyperplane, thereby confirming the stability of the SVM model.

Disadvantages of Support Vector Machine (SVM)

1. Choosing an appropriate Kernel function is difficult: Choosing an appropriate Kernel function (to handle the non-linear data) involves complexity. What happens is –  when you use a high dimensional kernel function, you might end up generating too many support vectors and that reduces the training speed. 

2. Extensive memory requirement: You obviously need a lot of memory to store all the support vectors in the memory. This number keeps on growing with the training dataset size.

4. Long training time: SVM requires a long training time on large datasets.

What you learned here is only a fraction of the SVM’s potential. Machine Learning algorithm is a fascinating field to dive into. SVM, even more. You can imagine what exploring this field can do to you.

For further reading, learn more about data science here and see what data scientist does.

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.