While many industries are struggling amid the coronavirus pandemic, both the IT industry and the broader trend of transition to remote work have revealed many areas where traditional approaches to managing businesses create unnecessary waste. Still, data science and its subdivision – machine learning – reveal that such expansion is nearly limitless.
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Machine learning uses powerful algorithms to discover insights based on real-world data that can then be used to make predictions about future outcomes. As new data comes available, machine learning programs can automatically adapt and produce updated predictions. As with any tool, machine learning is not a silver bullet. However, there are many situations in which the technology can outperform linear and statistical algorithms.
Here are five of the most common use cases where machine learning can make a big difference:
When engineers can’t code rules for certain problems
Many human-oriented tasks (such as recognising whether an email is spam) aren’t solvable using simple (deterministic), rule-based solutions. Because so many factors may influence an answer, engineers would have to write and frequently update billions of lines of code. In addition, when rules depend on too many factors, and when those rules overlap or need fine-tuning, it becomes difficult for humans to code precise rules. Fortunately, machine learning programs don’t require users to encode actual patterns. These programs only need proper algorithms to extract patterns automatically.
When you need to scale a solution to millions of cases
You might be able to manually categorise a few hundred payments as either fraudulent or not. However, this becomes tedious or impossible when dealing with millions of transactions. As user bases grow, it’s no longer feasible for organisations to process payments by hand – end-users today want answers about their money in milliseconds, not minutes or hours. Machine learning solutions are effective at handling these types of large-scale problems with little or no human intervention. […]
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