Machine learning refers to computer programs in which the programs have the ability to learn. Computers are able to do this through analytical and statistical components written in the program. In supervised machine learning, a program is given a set of training examples through data and the program is then able to give conclusions with new data. In unsupervised machine learning, a program is not given the set of training examples and must draw conclusions on its own.
The following is an overview of applications of machine learning.
1. Facial recognition and imaging software are being developed in order to help physicians in identifying diseases like cancer.
2. Machine learning can detect fraud better than humans because they are able to better process large amounts of information. Machine learning is able to understand users purchasing habits and detect anomalies that may be fraud.
3. Recommendation engines are ubiquitous in consumer products. For example, Netflix recommends to users new shows, UberEats recommends new restaurants to try, and Amazon suggests products that their users may be interested in.
4. Self-driving cars use machine learning to improve the safety of its driving.
5. Social media analyzes a given user’s activity to generate content for them. For example, Instagram has a discover page that’s created based upon a user’s prior activity. Facebook analyzes users’ prior activity in order to generate advertisements that are targeted toward specific users.
Looking forward, machine learning’s capabilities are only going to expand. Companies are going to be able to segment their market more finely, which will result in advertisements and products that are more specifically geared towards specific groups. This will result in a user experience with greater personalization. Also, machine learning will be able to use big data in order to better predict outcomes. This could have applications with forecasting stock prices, the weather, or political outcomes.