The examples and applications of machine learning are so many, both in daily life and in business, that we no longer pay attention.

As a business owner, however, you shouldn’t fail to notice how rapidly and furiously this technology is developing and how it can impact your business in the years to come.

Just consider this:

  • Spending on artificial intelligence (AI) and machine learning (ML) will reach $57.6bn by 2021, up from $12bn in 2017 (IDC)
  • The global machine learning market is expected to grow to $8.81bn by 2022 (Research And Markets)
  • 74% of over 1,600 surveyed business owners, decision makers, and tech leaders consider ML a game changer, with the potential to transform their job and industry (MemSQL)

In other words:

AI and ML aren’t just buzzwords anymore, but, probably, the most important technology to invest in the 21st century.

By adopting the machine learning technology, your business can gain a considerable advantage over the competition by improving visibility into data, uncovering more insights from data, improving efficiency of external and internal processes (i.e. optimizing your value chain and operations), understanding your prospects and customers better, and considerably reducing cost.

All of that is doable if you know how to apply specific machine learning use cases to empower your business.

In this article, I’ll explain the practicalities of machine learning, provide an overview of industries rapidly adopting ML, and look into nine specific use cases of machine learning, from image and speech recognition to fraud detection and medical diagnosis.

Machine Learning in Practice

The power of machine learning is utilized behind the scenes:

Machine learning impacts business decisions, streamlines work processes, reduces overheads, and advances our everyday lives as a whole.

Examples of machine learning use cases

However, no matter how appealing the idea of ML may be, it can’t realistically solve every business problem, or turn struggles into successes.

In practice, the adoption of machine learning requires:

  • People. Every machine learning solution is designed, built, implemented, and optimized by a team of highly trained professionals: ML scientists, applied scientists, data scientists, data engineers, software engineers, development managers, and technical program managers. These skill sets can cost your business a fortune and are tough to find and hire.
  • Time. Conducting a machine learning project requires a significant amount of time, from a few weeks to several years. Your ML team will have to collect and clean data; design, build, test, and optimize the ML solution. All of these tasks require a serious time commitment.
  • Budget. Machine learning is a premium service. Your company should be financially solvent to hire and “upkeep” the machine learning team for at least several months.

That is to say, machine learning can easily be either a dead-end option or a winning ticket for your business. And here’s why it makes sense to look at industries and specific machine learning use cases before jumping on board.

Machine Learning Adoption in Different Industries

Given the complexity and high cost of machine learning solutions, it’ll hardly come as a surprise that the technology is primarily adopted in finance, banking, and healthcare.

According to the latest KDnuggets Poll asking about the application of analytics, data science, and machine learning, the surveyed indicated the following top 10 industries:

  1. CRM/Consumer analytics
  2. Finance
  3. Banking
  4. Healthcare
  5. Fraud detection
  6. Science
  7. Retail
  8. Advertising
  9. E-commerce
  10. Education

This breakdown is hardly surprising as well.

For instance, retail, e-commerce, and consumer analytics apply machine learning to forecast demand, optimize prices, segment customers, provide customer recommendations, detect and prevent fraud.

Machine learning use cases in retail

In finance and banking, machine learning is used for credit scoring, risk analysis, client analysis, trading exchange forecasting, and fraud detection.

Machine learning use cases in finance

In healthcare and life sciences, machine learning is applied to increase diagnostic accuracy, identify at-risk patients, optimize the cost of insurance products, and more.

Machine learning use cases in healthcare

Advertisers and marketers rely on machine learning, too. They use it to segment markets and customers, dynamically optimize prices, analyze churn rates, predict customer lifetime value, analyze upsell opportunity, and run sentiment analysis in social networks.

Watch the video to learn machine learning use cases in marketing:

The machine learning technology is versatile, though, and relies on various machine learning algorithms, processes, techniques, and models. Let’s look at specific use cases of machine learning to figure out how ML can be applied in your business.

9 Practical Machine Learning Use Cases Everyone Should Know About

1. Image & Video Recognition

Over the past few years, image and video recognition have experienced rapid progress due to advances in deep learning (DL), which is a subset of machine learning.

Image and video recognition are used for face recognition, object detection, text detection (printed and handwritten), logo and landmark detection, visual search, reverse image search, image composition, and image curation.

Machines are good at processing images. For instance, participants of ImageNet, one of many computer vision competitions, can now train DL models that recognize and classify images in the dataset far better than humans.

How image recognition works
Courtesy of Upwork

Video recognition is similar to image recognition in a sense that videos get broken down frame by frame and classified as individual digital images.

Companies using image & video recognition: Google, Shutterstock, Pinterest, eBay, Salesforce, Yelp, Apple, Amazon, Facebook.

2. Speech Recognition

Speech recognition is another area of machine learning that allows machines to “mimic” humans due to AI, ML, and deep learning techniques. In this case, however, not image pixels, or frame-by-frame videos, but audio files get analyzed and processed by neural networks to translate audio into a text file.

How voice recognition works

Speech recognition is used in search engines (e.g. Google, Baidu), virtual digital assistants (i.g. Alexa, Cortana, Siri, Google Assistant, AliGenie), smart speakers (e.g. Amazon Echo, Google Home), and voice-activated applications (e.g. Uber, Evernote).

3. Fraud Detection

According to The Nilson Report’s 2016 study, online credit card fraud was expected to cost banks and financial institutions $32bn in 2020. Unfortunately to the bad actors, ML’s fraud detection capabilities weren’t factored into the results.

In machine learning, fraud detection belongs to a separate class of classification problems, along with spam detection, recommendation systems, and loan default prediction.

To proactively detect fraud, ML models need to analyze transaction details in real time and classify a given transaction as legit or fraudulent, which, given enough data is provided, isn’t that complex to do.

ML use for fraud detection

Machine learning helps businesses save millions of dollars by detecting, flagging, and preventing fraudulent transactions.

4. Patient Diagnosis

Machine learning is extensively used in healthcare, offering doctors and health professionals tools to efficiently collect and analyze patients’ data for better diagnosis.

Efficient patient diagnosis is enabled by data, which comes in many shapes and sizes: MRIs, CAT scans, physician notes, pathology reports, bedside monitors, and more.

Types of data used in healthcare for machine learning

As of 2019, machine learning algorithms are capable of identifying cancerous tumors and skin cancer, diagnose diabetes, and most importantly, predict disease progression. No wonder that healthcare artificial intelligence market is expected to grow up to $34bn by 2025.

5. Anomaly Detection

Anomaly detection is widely used in manufacturing to increase productivity and efficiency, reduce costs, and optimize downtime.

Here’s how anomaly detection works:

  • Sensors are installed onto a piece of equipment to collect data
  • ML models process the data to find anomalous data
  • Anomalous data is analyzed to identify a specific problem pertaining to it
  • The problem is preemptively resolved to avoid equipment failure

Actually, this algorithm can be applied to a wide range of problems. For instance, credit card fraud, clinical diagnosis, structural defects are anomalies, which can be detected using machine learning.

Machine learning use cases of anomaly detection

Anomaly detection allows businesses to predict equipment failure to conduct maintenance and repairs, which cuts operational costs and saves lives. For instance, IoT sensors installed on aircraft aggregate and analyze data to report on components that need maintenance, which reduces the amount of plane accidents.

6. Inventory Optimization

Inventory optimization is one of the most unnoticed, yet crucially important use cases of machine learning. It enables the machines to control how much stock to keep and how to keep it in the warehouse in the most efficient manner, to ensure that the supply chain won’t run dry.

How ML works in inventory optimization

In other words, ML-powered inventory optimization ensures that your business preemptively stores enough products to sell (see Demand Forecasting below), that these products are efficiently stored and distributed, and that your customers get their purchases on time.

Amazon is the world’s leader in optimizing their inventory using machine learning. The company manages to ship an average of 1.6 million packages per day, with an impeccable order fulfillment accuracy.

7. Demand Forecasting

Demand forecasting is applied in a wide variety of industries, from e-commerce and retail to transportation and manufacturing. It feeds historical data to ML models to predict the amount of something — be it products, services, raw material, power, or anything else — to be consumed on the market in a given period of time.

It allows businesses to collect and process data from the entire supply chain, which increases efficiency and reduces overheads.

Extended supply chain information flow enabled by machine learning

ML-powered demand forecasting is rapid, accurate, and transparent. It allows businesses to generate insights from a constant stream of supply/demand data, and to preemptively adapt to changes. Demand forecasting is the most popular machine learning application for supply chain planning, according to Gartner.

Machine learning use cases in supply chain

8. Recommendation Systems

Recommendation or recommender systems are one of the most ubiquitous applications of machine learning in daily life. These systems are used in search engines, e-commerce websites (e.g. Amazon, eBay), entertainment platforms (e.g. Netflix, Google Play), games, and multiple Web & mobile apps.

Recommender systems are usually classified by the filtering method:

  • Content-based filtering method. This method recommends items to a user, based on items this particular user has engaged with. For example, if you’ve purchased a book about machine learning at Amazon, it’ll display more ML-focused books in the suggestions section.
  • Collaborative filtering method. In the collaborative filtering method, the recommendation system analyzes the actions and activities of a pool of users to compute a similarity index and to further display similar items to similar users.

However, there’re more advanced types of recommender systems as well.

History of ML recommender systems

Recommendation systems process various sources of data: searches, clicks, item views, page views, form fill-ins, purchases, return visits, as well as item details (e.g. title, price, category) and contextual data (e.g. device, location, language).

Recommender systems are a valuable asset to any business, since they allow to drive more traffic, deliver relevant content, increase customer engagement and conversions, reduce churn rate, and boost profits.

9. Intrusion Detection

ML-powered intrusion detection is the lifeblood of adaptive intrusion detection systems (IDS), which monitor networks in real time to identify and cope with malicious traffic or intrusion techniques, like brute force, infiltration, and unauthorized access.

Machine learning use case for intrusion detection

Machine learning has helped revolutionize intrusion detection. Traditionally, an IDS was designed to identify known threats. Bad actors could design a new intrusion method to bypass the system.

Now, however, network data is continuously collected and pre-processed to create high-quality datasets, which are used to train machine learning models that efficiently tell normal traffic from malicious traffic in real time.


The adoption of machine learning is increasing by leaps and bounds, and that’s not surprising given its benefits, from eliminating manual tasks to uncovering useful insights from data.

In this article, we’ve looked into specific machine learning use cases: Image & speech recognition, speech recognition, fraud detection, patient diagnosis, anomaly detection, inventory optimization, demand forecasting, recommender systems, and intrusion detection.

However, these are just the most common examples of machine learning. Other notable mentions are sentiment analysis, financial analysis, machine translation, statistical arbitrate, spam detection, research insight in education, time-intensive data entry, smart grid management, and more.

For sure, every machine learning use case is different in design, architecture, tooling, and optimization. To top it off, data availability, data quality, data storage, processing power, and many other factors play a role.

If you’re looking to empower your business with machine learning, reach out to Squadex ML professionals for help. We’ll audit your project, outline a technical roadmap, develop a PoC, come up with cloud architecture options, and develop your ML solution on time.

To learn more about our ML solutions, visit Case Studies page.