Machine Learning in Business: Issues for Society
AT ITS CORE, machine learning is concerned with using large data sets to learn the relationships between variables, make predictions and interact with a changing environment. And it is becoming an increasingly important tool in business — so much so that almost all employees are likely to be impacted by it in one way or another over the next few years.
Large data sets on variables describing consumer purchases, stock price movements and many other aspects of a business are not new. What is new is that advances in computer processing speeds and reductions in data storage costs allow us to reach conclusions from large data sets in ways that were simply not possible 20 or 30 years ago.
Machine learning, also referred to as data science, can be viewed as the new world of statistics. Traditionally, statistics has been concerned with such topics as probability distributions, confidence intervals, significance tests and linear regression. Knowledge of these topics remains important, but we are now able to learn from large data sets in new ways. For example:
• We can develop non-linear models for forecasting and improved decision making;
• We can search for patterns in data to improve a company’s understanding of its customers and the environment in which it operates; and
• We can develop decision rules where we are interacting with a changing environment.
These applications of machine learning are now possible because of increases in computer processing speeds and reductions in data storage costs. And as a result, data science may well prove to be the most rewarding and exciting profession of the 21st century.
Data science may well prove to be the most exciting profession of the 21st century.
My latest book, , explains the most popular algorithms used by data scientists. The objective is to enable readers to interact productively with data
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