Machine Learning

 

Machine Learning is a subset of Artificial Intelligence (AI) that provides computers with the ability to learn without being explicitly programmed and to take intelligent decisions. It also enables machines to grow and improve with experiences.

It has various applications in science, engineering, finance, healthcare and medicine. Some applications of Machine Learning are given below.

Applications of Machine Learning:

o    Predictive maintenance or condition monitoring

o    Warranty reserve estimation

o    Propensity to buy

o    Demand forecasting

o    Process optimization

o    Telematics

  • Retail

o    Predictive inventory planning

o    Recommendation engines

o    Upsell and cross-channel marketing

o    Market segmentation and targeting

o    Customer ROI and lifetime value

  • Healthcare and Life Sciences

o    Alerts and diagnostics from real-time patient data

o    Disease identification and risk stratification

o    Patient triangle optimization

o    Proactive health management

o    Healthcare provider sentiment analysis

  • Travel and Hospitality

o    Aircraft scheduling

o    Dynamic pricing

o    Social media-consumer feedback and interaction analysis

o    Customer complaint resolution

o    Traffic patterns and congestion management

  • Financial Services

o    Risk analytics and regulation

o    Customer Segmentation

o    Cross selling and up selling

o    Sales and marketing campaign management

o    Credit worthiness evaluation

  • Energy, Feedstock and Utilities

o    Power Usage analytics

o    Seismic data processing

o    Carbon emission and trading

o    Customer-specific pricing

o    Smart grid management

o    Energy demand and supply optimization

Advantages of Machine Learning-

  • Useful where large scale data is available
  • Large scale deployments of Machine Learning beneficial in terms of improved speed and accuracy
  • Understands non-linearity in the data and generates a function mapping input to output (Supervised Learning)
  • Recommended for solving classification and regression problems
  • Ensures better profiling of customers to understand their needs
  • Helps serve customers better and reduce attrition

And many more………

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