Real-Life Machine Learning Use Cases

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!

Conclusion

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!).