K-means clustering is a type of clustering model that takes the different groups of customers and assigns them to various clusters, or groups, based on similarities in their behavior patterns. Similar systems are what power Amazon's "you might also be interested in" carousel. In this scenario, even though we don't have explicit, definitive data on what a person's interests are, just identifying that a particular group of customers purchase similar items can allow us to make purchase recommendations based on what other people in the cluster have also purchased. Given customers' purchase history, can we identify any clusters (groups of similar customers)? In unsupervised learning problems, the data we're given has no labels, and we're simply looking for patterns. For example, predicting the price of a house given the number of bedrooms and its location. If, however, our target variable is continuous, then the problem is referred to as regression. then the problem is referred to as classification, as we're trying to determine the class that a given data point belongs to. If the target is a choice between a few discrete categories - for example, will the applicant default or not, is this a picture of a cat, a dog, or a human, etc. This difference refers to the type of quantity our target is. Supervised learning algorithms can be further subdivided into regression and classification. Here, we had historical data about past loan applicants' credit scores (and potentially income levels, age, etc.) alongside explicit labels which told us if the person in question defaulted on their loan or not. However, there are ways to automate the labeling process.Ī great example of supervised learning is the loan applications scenario we considered earlier. Labeling is typically performed by humans, which can be expensive and time-consuming. Labeling is the process of annotating examples to help the training of a machine learning model. If you’re dealing with unlabeled data, you’ll need to do data labeling. Semi-supervised learning uses a combination of labeled and unlabeled data to train AI models. Supervised machine learning refers to classes of algorithms where the machine learning model is given a set of data with explicit labels for the quantity we're interested in (this quantity is often referred to as the response or target). Machine learning algorithms are often divided into three general categories (though other classification schemes are also used): supervised learning, unsupervised learning, and reinforcement learning. 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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