Over a million developers have joined DZone.

Real-Life Machine Learning Use Cases

DZone's Guide to

Real-Life Machine Learning Use Cases

Building a machine that is a replica of the human brain and that meets the expectations of billions of users isn't easy — but here are some places where this complicated task is being tackled.

· AI Zone ·
Free Resource

Bias comes in a variety of forms, all of them potentially damaging to the efficacy of your ML algorithm. Read how Alegion's Chief Data Scientist discusses the source of most headlines about AI failures here.

The power to think, decide, and act based on the situation, emotion, and person is something that makes human a unique species in the ecosystem.

A few principle thoughts to get us started:

  • Machine learning is like building the human type of behavior in a non-living object, machine, or system based on some highly rich and complex algorithms and techniques.

  • Machine learning helps me know what I should take and fits my taste before I myself realize and ask for the same.

Before diving deep into the details and granularity of machine learning features, let’s get a general feel for it and discover where, in our day-to-day real lives, machine learning is important and makes sense:

  • Banking, retail, and telecommunication
    • Prospective customers and partners
    • Satisfactory index of the customer (based on relationships, transactions, marketing campaigns, etc.)
    • Fraud, waste, and abuse of claims
    • Forecasted credit risk and credibility of the customer
    • Effectiveness of a marketing campaign
      • For example, how many accepted the offer and how many rejected it? Were there any decisive factors leading to acceptance?
    • Cross-selling and recommendations
      • For example, e-commerce sites that tell you, "People who purchased this product also purchased this."
    • Contact center (helps the customer service representative engage the customer during the call with relevant data)
      • For example, "We see that you have ordered checkbooks to an address different from what we have on file; would you like to change your address details?"
  • Healthcare and life sciences
    • Scanning, screening, and biometrics
    • Drug discovery based on the component mix
    • Diagnosis and remediation based on symptoms, patient records, and lab reports
    • AECP (Adverse Event Case Processing) scenarios based on data about drugs, patients, geolocation, climatic conditions, past history, food intake, etc.
  • General
    • Handwriting to Text or Speech (Identification & Learning Graphology Techniques)
    • Debugging, Troubleshooting and Solution Wizard
    • Email filtering based on Spam
    • Text & Mail categorization/recommendation
    • Support Issues and enriching KeDBs (Knowledge Error Databases)
    • Friends and Colleagues Recommendation – via Facebook, LinkedIn, Twitter etc.
    • Self-Driving Cars – by building artificial intelligence and algorithms
    • Image Processing
  • Security
    • Handwriting, signatures, fingerprints, iris/retina identification and verification
    • Face recognition
    • DNA pattern matching

Feel free to suggest and help me enrich this list based your experience and the real-time scenarios you have witnessed!


Building something with a machine/non-living object that is a replica of the human brain and that caters to meet the expectations of billions of users is not an easy job. A rich volume of quality data combined with flawless algorithms is critical for building and training a machine learning model to think, decide, and act like humans do.

With billions of non-stop data processing, the human mind can get tired. This is where machine learning algorithms play a crucial role. Build once and automate it.

In simple words: big data + machine learning = deadly duo (in the best way possible!).

Your machine learning project needs enormous amounts of training data to get to a production-ready confidence level. Get a checklist approach to assembling the combination of technology, workforce and project management skills you’ll need to prepare your own training data.

machine learning ,ai ,algorithms

Published at DZone with permission of

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}