Next, let's consider the different types of machine learning algorithms and the specific types of problems they can solve. From self-driving cars to voice recognition to the automated email filtering systems that flag the spam in your inbox, machine learning algorithms form the basis of many of the advances in technology that we've come to depend on today. Modern approaches to machine learning have made great strides and can accomplish a lot more than just that. Given a historical customer dataset, for example, you could predict which of your current customers are in danger of leaving, so you can stop churn before it happens. Still, it's a great example of just how powerful machine learning can be.Īny organizational KPI can be optimized as long as you have the relevant data. This is just an introduction to machine learning, of course, as real-world machine learning models are generally far more complex than a simple threshold. In doing so, the machine is learning from historical data and creating its own rules. In a rules-based approach, the bank manager (or other experts) would explicitly tell the computer that if the applicant's credit score is less than a threshold, reject the application.Ī machine learning algorithm, however, would simply take in historical data on the credit scores of customers and their loan outcomes and figure out, on its own, what this threshold should be. Say you're a bank manager, and you'd like to figure out whether a loan applicant is likely to default on their loan.

Where machine learning differs from and supersedes, rules-based programming is that it's capable of inferring these rules on its own. This method of programming computers is referred to as being rules-based. Let's contrast this with traditional computing, which relies on deterministic systems, wherein we explicitly tell the computer a set of rules to perform a specific task. These inferences are often based on using algorithms to automatically examine the statistical properties of the data and creating mathematical models to represent the relationship between different quantities.

Machine learning is a branch of computer science that allows computers to automatically infer patterns from data without being explicitly told what these patterns are.

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