Machine learning continues to be a hot topic in modern enterprises. It sounds futuristic, but really, it’s just the process of building and training computer models to process data. Big data is one of the drivers behind the growing adoption of machine learning. We are producing so much data that it’s no longer practical for humans to sift through it all, identify patterns and make decisions based on it. Humans also can’t do it fast enough.
Enter machine learning
With machine learning, businesses can take advantage of the insight that’s locked in all of their data to identify patterns and make decisions quickly. The bottom line is that machine learning can do more than humans and machine learning can do it faster.
Machine learning is already all around us, influencing everything from the music we hear to the employees we hire. How does machine learning work in the real world, and how can it help your business? Here are 25 common examples of machine learning.
1. Music recommendations
This is one of the more common examples of machine learning that we encounter every day. Music apps like Spotify and Pandora can make artist recommendations for you. The recommendation algorithm looks at what you’ve listened to in the past and combines that data with information about artists who are typically discussed in blogs and articles. The machine learning model can then recommend new artists that are similar to the ones you already like.
2. Movie recommendations
Similarly, streaming content services like Netflix can make suggestions about movies and shows you might like based on what you’ve watched in the past. The algorithm uses your viewing history combined with data from other viewers who are watching some of the same shows as you to make some educated guesses on what you might want to enjoy next.
3. Friend/account recommendations
Social apps like Facebook and Instagram can find people you might know. Using information such as your current friends, the friends of your friends, and other data such as where you live, where you went to school and what your interests are, social networking apps can find and suggest people you might know or accounts you might want to follow.
4. Facial recognition
Facial recognition has made big leaps in sophistication in the last few years. Your phone can automatically create a group of photos that feature a certain person. Facebook can suggest – with a startling degree of accuracy – who you might want to tag in a picture you’ve just uploaded. Behind the scenes, machine learning is analyzing the photos and learning how to match people up to photos they’ve previously been tagged in.
5. Speech recognition
Until relatively recently, speech recognition apps were hit or miss, often with unintentionally hilarious results. Today, machine learning algorithms can learn to recognize spoken words and translate them to text with a high degree of accuracy. One application of this example of machine learning is automated transcription services.
6. Spam filters
Spam filters can figure out whether or not a message is spam based on characteristics in the subject line, body and return email address.
7. Predictive maintenance
Manufacturers can predict when a piece of machinery will need maintenance based on the maintenance and repair that was required on similar machinery in the past.
8. Credit checks
Credit card companies and banks use financial information to quickly and accurately determine credit worthiness of applicants and mitigate their risks.
9. Personalized offers
Say you log on to pay your credit card bill and you get an offer for a balance transfer deal. Machine learning has determined that you are a good candidate for that balance transfer.
10. Customer retention
Your bank, wireless provider, gym, or other business can use data about your activity to determine if you might be getting ready to take your services elsewhere. For example, a customer who has stopped using a credit card may be getting ready to close the account. The company might respond by offering a lower APR on purchases made in the next six months or take other measures to retain the customer.
11. Fraud detection
Banks or retailers can identify potentially fraudulent transactions using information about customers and typical transactions for them. Transactions that don’t fall into existing parameters for the amount of the transaction, the location where it originated, and other factors, can indicate that the transaction might be fraudulent.
12. Product recommendations
Companies use machine learning to identify products that you might like based on your previous purchases and what you’ve got in your shopping cart.
13. Targeted ads
Have you ever visited a website, looked at a product, and then had that product show up in emails or other ads? Companies can use machine learning to deliver targeted ads to potential buyers, increasing their chances of making a sale.
Many companies are looking for ways to simplify and streamline their customer service processes. The use of chatbots is one way to take some of the burden off representatives and help solve customer service issues using machine learning.
15. Virtual personal assistants
Siri and Alexa are two well-known examples of virtual personal assistants. They use natural language processing to respond to questions, gather information and respond to you. Machine learning helps them “learn” more about you and your preferences over time.
16. Traffic apps
Using data gathered during previous commuting times, map and traffic apps can predict how long it will take you to get from A to B.
17. Self-driving cars
Self-driving cars use machine learning to process all of the data gathered from the car’s sensors to predict what cars around you might do and how to proceed safely.
18. Medical diagnoses
Doctors are using machine learning to quickly analyze a patient’s symptoms, plus factors such as age and medical history, to assist with making diagnoses.
19. Medical image processing
Similar to the way facial recognition works, machine learning technology can help radiologists analyze x-rays and other medical images. Machine learning algorithms can be trained using images from numerous patients, learning which images were normal and which ones were potentially abnormal.
20. Renewable energy forecasting
Energy companies are using machine learning to determine how to maximize wind and solar energy. Wind and sun energy output isn’t always consistent, but using machine learning algorithms, energy companies can determine how to more accurately predict wind and solar conditions.
21. Real estate valuation
Machine learning algorithms can take data about real estate and use it to estimate the current valuation. Valuation is typically based on factors such as the value of comparable real estate in the same area. Machine learning can take in all of this data along with information about features such as number of bedrooms to make an accurate prediction about valuation.
22. Learning apps
Educational apps use machine learning to help their users learn. For example, Duolingo uses machine learning algorithms to analyze data gathered from its users to adjust the pace of its courses as users learn new languages.
23. Refining search engine results
Search engines such as Google use machine learning to improve your search results. When you perform a search, machine learning analyzes how you respond to the results. If you spend a lot of time at a page that was further down in the rankings, the algorithm may bump that result up next time. The algorithms are constantly working to deliver the best, most accurate results.
Machine learning algorithms can be used to detect incidents that might indicate a security threat, such as more than five attempts to log in to an account.
25. Candidate screening
When companies receive hundreds of resumes for a job listing, it’s not always possible to have a human review them all. Companies can train machine learning algorithms to scan resumes for key words and help determine if a candidate should be advanced for further consideration.
Want to learn more about machine learning?
Machine learning can help your business process and understand data insights faster, empowering users to make data-driven decisions across your organization. For machine learning to be successful, however, you need high volumes of high-quality data to feed and train models. As the quality of your data increases, you can expect the quality of our insights to increase as well. To learn more, check out some of our other machine learning resources.
Matillion and machine learning
Transforming data to ensure it is useful for machine learning, analytics, and other uses can be challenging based on the growing volume, variety, and velocity of big data. Matillion is data transformation that is built for the cloud, with the ability to transform large volumes of data quickly and at scale.
Request a demo to learn more about how you can unlock the potential of your data and machine learning with Matillion’s cloud-based approach to data transformation.
If you need to get your data into the cloud in preparation for machine learning, Matillion Data Loader makes it simple to replicate your data into a data lake, lakehouse, or cloud data warehouse. A SaaS-based, free data integration tool, Matillion Data Loader includes a number of integrations and gets your data into the cloud in just a few clicks.