Real-Life Machine Learning Use Cases
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.
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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.
- 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
- 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!).
Published at DZone with permission of Pritiman Panda , DZone MVB. See the original article here.
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