In simple terms, machine learning is the method to make computers through programming (algorithms) understands the structure of data; or in other words: automatically learn from experience (patterns) and act upon it without human intervention. Machine learning operates with data, hence its link to big data: it is used with data at a large scale when data is too complex, or it keeps changing.
Its goal is to: deliver faster and more accurate results and of course, that it learns more efficiently.
Its importance relies on the growing volumes of data and its varieties, making processing quickly, automatically and cheaper. A machine is more able to quickly highlight or find patterns than a human being. With machine learning, a company has the advantage to:
- Detect important insights in data and build models that uncover connections (i.e. fraud, money laundering, terrorism)
- Identify profitable opportunities (promote items a customer would be interested in or understanding customer behavior)
- Avoid risks
- Better decision making
But as any issue, it has risks. One is “trash-in, trash-out” if it operates with data and the income is inaccurate (i.e. skewed data or insufficient features describing the data) what do you think the outcome is going to be? Inaccuracy, indeed. Another risk: that with new data it doesn’t adjust or adapt itself independently. In this case, parameters should be checked. Lastly: once in a while check how the machine is interpreting data. You don’t want an incorrect assumption.
For some people is a threat: machines are replacing us. The truth is that they are a complement: can enhance our abilities to solve problems and make better-informed decisions.
Machine learning is closer to you…think about self-driving cars…or when you call the bank and a system answers you; it even greets you because it recognized your voice…ads in your computer… Make it work for you!
By Mónica Ramírez Chimal, Partner of Asserto RSC, Mexico City
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