How Does Machine Learning Work?
Machine learning is made up of three parts:
- The computational algorithm at the core of making determinations.
- Variables and features that make up the decision.
- Base knowledge for which the answer is known that enables (trains) the system to learn.
Initially, the model is fed parameter data for which the answer is known. The algorithm is then run, and adjustments are made until the algorithm’s output (learning) agrees with the known answer. At this point, increasing amounts of data are input to help the system learn and process higher computational decisions.
Why Is Machine Learning Important?
Data is the lifeblood of all business. Data-driven decisions increasingly make the difference between keeping up with competition or falling further behind. Machine learning can be the key to unlocking the value of corporate and customer data and enacting decisions that keep a company ahead of the competition.
Machine Learning Use Cases
Machine learning has applications in all types of industries, including manufacturing, retail, healthcare and life sciences, travel and hospitality, financial services, and energy, feedstock, and utilities. Use cases include:
- Manufacturing. Predictive maintenance and condition monitoring
- Retail. Upselling and cross-channel marketing
- Healthcare and life sciences. Disease identification and risk satisfaction
- Travel and hospitality. Dynamic pricing
- Financial services. Risk analytics and regulation
- Energy. Energy demand and supply optimization