What Is Pattern Recognition?

Key takeaways

• Pattern recognition involves using supervised or unsupervised learning techniques to identify trends in data.

• Reliable training data is vital for effective pattern recognition in machine learning. 

• Pattern recognition streamlines data analysis and has applications in speech recognition, data mining, and image processing. 

You can learn and implement pattern recognition by pursuing job roles such as a machine learning engineer or data analyst, or through certification programs, such as Microsoft Certified: Power BI Data Analyst Associate. Discover more about pattern recognition, including the careers that utilize this process.

If you’re ready to get your start in pattern recognition right away, enroll in the Natural Language Processing Specialization by DeepLearning.AI. You’ll have the opportunity to learn how to use logistic regression and recurrent neural networks for sentiment analysis, text generation, and named entity recognition.

What is pattern recognition?

In machine learning (ML), pattern recognition is the process of discovering similarities within small problems to solve larger, more complicated problems. Pattern recognition techniques are crucial in intelligent systems and prove useful in many application domains.

Pattern recognition incorporates two distinct learning classifications: supervised and unsupervised. For supervised learning, humans label a set of organized training data, giving it to a computer in an attempt to find relationships. Conversely, unsupervised learning involves a computer finding correlations in unlabeled data without help from humans. Semi-supervised learning can also be valuable in training, where both labeled and unlabeled patterns help a computer learn.

Read more: Supervised vs. Unsupervised Learning: Pros, Cons, and When to Choose

Types of pattern recognition

Some popular approaches to pattern recognition are statistical pattern recognition, neural pattern recognition, template matching, and syntactic pattern recognition.

• Statistical pattern recognition: Classifies patterns based on an underlying statistical model.

• Neural pattern recognition: Functions by classifying based on the neural response of input patterns and requires very little prior knowledge to work.

• Template matching: Recognizes patterns by matching observed objects to stored images.

• Syntactic pattern recognition: Classifies patterns based on estimated structural similarity.

What is pattern recognition in humans?

Pattern recognition in humans is the cognitive process of identifying patterns, such as certain sounds or objects, allowing you to recognize and accurately interpret things in your environment. For example, your ability to recognize patterns in clothes, such as similar shapes and sizes, allows you to fold and sort clean laundry, making sure to place the socks, long-sleeve shirts, short-sleeve shirts, and pants in individual stacks. This differs from pattern recognition in machines, where algorithms work to identify patterns within data, rather than the real-world.

What is pattern recognition used for?

Pattern recognition has a wide variety of applications, which include the following:

• Image processing: Image processing utilizes pattern recognition and often a specific classification scheme to explore how to recognize image patterns.

• Video processing: Pattern recognition helps analyze videos to identify people, detect objects, and enable autonomous driving.

• Speech/audio recognition: Text-to-speech converters and digital assistants such as Apple’s Siri use pattern recognition to analyze voice cues and understand what different words and phrases express.

• Natural language processing: You can use pattern recognition to describe language patterns to a computer and teach it how we speak and understand human language.

• Data mining: Pattern recognition is integral for extracting valuable information and patterns from sizable data sets.

• Bioinformatics: To interpret biological data, you can use pattern recognition to create protein classification systems, accounting for elements like structure, sequence, biomolecular interaction, and subcellular localization.

Where is pattern recognition used in real life?

Many careers and individuals use pattern recognition, including:

• Retailers: Audit products using image recognition or self-checkout to recognize purchases.

• Financial analysts: Identify insights into market trends and predict stock changes.

• Researchers: Identify recurring patterns in the Earth’s seismic activity.

• Law enforcement professionals: Sort through police data and discover patterns and relationships between crimes.

You could also utilize pattern recognition as a crime analyst, medical professional, machine learning engineer, or data analyst. Next, you can read more about what these professionals do in the context of pattern recognition, along with salary information for each.

All salary information represents the median total pay from Glassdoor as of August 2026. These figures include base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation.

Crime analysts

As a crime analyst, you observe historical crime patterns, referencing data to do so. This role calls for using advanced computing technology and data analysis software. Computational criminology utilizes pattern recognition to identify existing and emerging crime patterns, which helps predict where and when crimes will occur and who is likely to commit them.

Crime analyst median annual US salary (Glassdoor): $81,000 [1]

Medical professionals

In the medical field, radiology involves diagnosing and treating injuries and diseases with radiology tests like X-rays, CT scans, MRIs, and more. As a radiologist, pattern recognition can help you detect abnormalities, tumors, and other medical conditions when analyzing medical images. Pattern recognition also provides medical professionals with vital tools for health care analytics, such as risk prediction, disease progression prediction, and patient subtyping.

Radiologist median annual US salary (Glassdoor): $248,000 [2]

Clinician median annual US salary (Glassdoor): $145,000 [3]

Machine learning engineers

If you choose to become an ML engineer, you will work with technology-based programs and applications to solve problems experienced by those using said technology, all with the goal of training your systems to learn without human input. ML engineers use pattern recognition to match information stored in databases with incoming data by finding similarities between them.

Machine learning engineer median annual US salary (Glassdoor): $164,000 [4]

Data analysts

In this role, you identify ways to use data to solve business-related problems. You’ll apply pattern recognition to discover trends and insights in data, which you can then use to make recommendations to clients. You can also expect to spend some time creating algorithms in R and Python to help manipulate and interpret data.

Data analyst median annual US salary (Glassdoor): $94,000 [5]

Pros and cons of pattern recognition

Due to the complicated nature of pattern recognition, you can find both pros and cons with its application.

Pros

• Is fast and efficient: Pattern recognition streamlines data analysis, helping data analysts accomplish more in a shorter period.

• Is good at predicting: Capable of summarizing data patterns, pattern recognition is great at making accurate predictions on various data.

• Has wide applications: Pattern recognition has various applications across several different tools, which include statistical data analysis, probability, computational geometry, machine learning, and more.

Cons

• Depends on data accuracy: Training data must come from reliable sources for pattern recognition to work efficiently in machine learning.

• Comes with computational constraints: Pattern recognition algorithms continue to grow more computationally expensive, potentially limiting greater expansion of deeper learning in the future.

• May lead to overfitting: When overfitting occurs, a generalized pattern recognition model can perform well on training data but cannot reach the same success on a new data set.

How to learn pattern recognition and machine learning

You can prepare for a career that uses pattern recognition by getting an education and experience in fields that apply it. As stated above, ML engineers and data analysts are two job roles that heavily use pattern recognition. Below is more information on how you can start your career in these fields.

Education and experience

To become an ML engineer, the skills you will likely need to build include teamwork, programming, testing, troubleshooting, and platform optimization. You can gain these skills through direct experience in entry-level jobs and internships or by shadowing professionals. Taking additional training courses to expand your knowledge will also help you become experienced in these areas. For education, you’ll want to look into a degree in computer science, artificial intelligence, or data science.

If you want to become a data analyst, experience with statistical analysis, programming, data preparation, and data visualization is essential. Consider participating in undergraduate coursework and supplementary online courses to grow these skills. Learning the programming languages relevant to data analysis, such as Hadoop, JavaScript, Python, structured query language (SQL), and extensible markup language (XML), can help you become a more effective data analyst. Additionally, most employers require a bachelor’s degree in computer science or a similar field.

Certifications

You might want to earn a certification to further your skills within pattern recognition-related careers. You can look at Google’s Professional Machine Learning Engineer Certification, the Microsoft Certified: Power BI Data Analyst Associate certification, or the IT Specialist – Computational Thinking certification with Certiport to build up your resume.

Check out our free resources on machine learning

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