Real-World Machine Learning Use Cases

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The machine learning applications are almost endless, as we are slowly integrating ML-driven technologies into multiple aspects of our lives. Businesses from across industries are recognizing its advantages in streamlining processes, extracting insights from unstructured data, and automating labor-intensive tasks, leading to more accurate results and increased efficiency.

If all of this machine learning talk was a bit too abstract so far, you’re about to see you are not just familiar with the ML concepts, but likely utilize the benefits on a regular basis.

Personalized products and services

Machine learning models are regularly utilized to understand shopping habits (both when you shop in-store or online). Any buyer data collected is typically used to optimize business and marketing operations: from organizing products in brick-and-mortar stores to suggesting similar items on the eCommerce website and planning the marketing campaign.  

And the ML-fueled personalization doesn’t end with traditional consumerism.

Music streaming services like Spotify rely on machine learning models to deliver personalized experiences, empower content discovery, and ultimately enhance user engagement (or from the user’s perspective, it will take you mere minutes to build your first Country mix as soon as Beyonce’s new album drops).

All that thanks to ML algorithms that can categorize and tag music content based on different characteristics (genre, tempo, mood, and instrumentation). They are also capable of performing audio analysis to help improve quality, identify tracks, and remove duplicates or low-quality content from their library.

Enhanced customer feedback and satisfaction

Big players in the customer service industry like Zendesk and Intercom integrate machine learning with customer support tools to enable efficient analysis of emails, live chats, and other data sources. Over time, they automate support ticket tagging and routing to ensure timely responses to customer queries. 

If you add NLP to this mix, you enable machines to interpret and process human language, allowing for effective text analysis across various channels.

Smarter financial planning and investments

Machine learning algorithms are increasingly used in financial planning and investment to analyze historical data and identify patterns and behaviors, helping to recognize trading opportunities. 

Unlike humans, who may be influenced by emotions, machine learning employs sentiment analysis to objectively assess investment opportunities, minimizing biases. Additionally, these algorithms automate tasks, saving time for traders and allowing them to focus on strategic decision-making rather than constantly monitoring screens.

Accurate medical diagnostics and treatment

During the COVID-19 pandemic, we got to see just how big of a role AI and ML played in predicting the virus's spread, aiding in containment efforts, and facilitating patient diagnosis and risk assessment. This specific use case showed us how machine learning algorithms could analyze CT scans to detect abnormalities and employ facial recognition technology to identify individuals with fevers, potentially indicating infection. Furthermore, ML technology assisted in identifying patients at higher risk of developing severe respiratory illness, enabling proactive intervention and management.

Beyond the pandemic response, machine learning continues to drive healthcare innovation and ensure the field provides each patient with:

  • Personalized treatment plan, composed based on the advanced analysis of their data and medical history.
  • Accurate prediction of disease progression by identifying patients at risk of developing complications, allowing for proactive interventions and preventive measures.
  • Reduced bias by training machine learning models with labeled data to provide accurate predictions on unseen data,  reducing errors and biases inherent in human interpretation.
  • Always-available health care, by empowering clinicians with tools and technologies that will enable them to provide their services to anyone, at any time. 

Ultimately, machine learning should deliver more efficient healthcare services at a reduced cost.


Machine learning is increasingly being integrated into gaming experiences by companies like Xbox and PlayStation (PS4) to enhance different aspects of gameplay, user engagement, and overall gaming experience.

Machine learning algorithms analyze player behavior, preferences, and gaming history to provide personalized game recommendations. By analyzing a player’s skill level, performance, and in-game interactions, ML algorithms dynamically adjust the difficulty level and use the data to improve NPC (non-player character) behavior, game balancing, and overall game design.

And likely players’ favorite use of machine learning – to install effective anti-cheat and security measures in place. ML can be utilized to detect and prevent cheating, hacking, and unauthorized activities in online multiplayer games. By analyzing player behavior and gameplay patterns, machine learning algorithms can identify suspicious activities and enforce fair play policies.

Efficient orders, deliveries, and pickups

How often do you Uber or place orders via UberEats? At the core of their solutions are machine learning algorithms utilized to improve their services and operations, like:

  • Ride demand prediction in different areas and times to enable efficient disputes and reduce waiting times
  • Ride matching based on multiple factors, like location, traffic conditions, and driver availability
  • Routing and navigation to optimize suggested routs while accounting for traffic patterns, road closures, and the estimated time of arrival
  • Dynamic pricing and fare adjustment based on demand and supply
  • Fraud detection and safety to prevent fake accounts, payment fraud, or suspicious behavior during rides

Photo by Markus Winkler