in data science, machine learning

3 Machine Learning Applications for Businesses

Machine learning is about computers (the machines) having the ability to learn without you explicitly programming those. It’s powerful because it leads to automation and making sense of the data (especially with large data sets) can be much easier.

How businesses can use machine learning? Here are 3 ways:

1. Give personalized recommendations to customers

Customers have different buying patterns and product preferences. Through machine learning, computers can learn those and give personalized recommendations to customers.

It’s how Amazon works. After purchasing a product (or even while browsing their site), you see recommendations at the bottom (“Customers also bought…”). These recommendations are tailored according to the customers’ past purchases and other factors.

2. Clustering of data

With thousands and millions of data points, the data seems random. But with machine learning, you can still derive patterns and extract insights by clustering the data.

For example, learning about your thousands of customers seems overwhelming. Machine learning can help you classify them into subsets. Even if the data points seem random, customers can still share some degree of similarity. Once you have your subsets, you can then design products or customize your services according to the patterns you see.

3. Anomaly detection

Here’s how it works. In a large set of data points, there might be some that significantly deviate from the average. They can be called outliers because they don’t fit into the data.

How is that useful? Through machine learning, we can know if something is suspicious or just out of the ordinary. This is helpful in manufacturing (specifications should not exceed a certain range) and fraud detection (a few transactions can be suspicious).

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Those are just 3 ways how machine learning can help businesses. The possibilities can be endless because we have powerful tools and we can now gather more data than is possible before.