9 examples of machine learning in action

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9 examples of machine learning in action

The term “machine learning” makes it sound like computers will solve problems for us without much human guidance. We’re not quite there yet. But, some fascinating careers are paving the way for artificial intelligence to help us all out in our daily lives and at work.

Machine Learning Engineers and Data Scientists that specialize in machine learning get to work in pretty diverse industries. That’s one of the best things about a career in programming or data science — you can take those skills just about anywhere. It also means that you can work in a field that excites you or one in which you feel like you’re making a positive contribution.

This article will show you examples of machine learning in action. We’ll also help you understand what machine learning is used for and how you can learn the skills required to be a part of this exciting industry.

What is machine learning used for anyway?

Machine learning isn’t as hard to understand as you might think. In short, it involves using pattern recognition software to find trends in data, building models that explain the trends/patterns, and then using the models to predict something. The more a computer program “learns” about a data set, the better it predicts the outcome of a new set of data.

For example, if you fed a machine learning algorithm a bunch of images containing flowers or people, it would learn from the labeled data and be able to discern whether the next image it processed was a flower or a person. In effect, it gets better the more it’s used because each new piece of data is a “learning” opportunity for the machine.

In another post, Hillary Green-Lerman, one of our Data Scientists, takes a closer look at what machine learning is, explaining how:

“Machine Learning is about using the data you already have to make predictions. This sounds really fancy, but most of the time, the ‘prediction’ is really just a label.”

9 machine learning examples

Machine learning careers are on the rise, so this list of machine learning examples is by no means complete. Still, it’ll give you some insight into the field’s applications and what Machine Learning Engineers do.

1. Image recognition

As we explained earlier, we can use machine learning to teach computers how to identify an image’s contents. You know when you’re asked to find all the buses, crosswalks, or traffic lights in a series of nine pictures online? You’re not just verifying you “aren’t a robot,” you’re actually helping to train a machine learning algorithm on image recognition with your answers.

2. Speech recognition

Speech recognition is being improved by machine learning algorithms as well. The number of applications that use speech inputs is staggering. From your word processor to your smart speaker to the automated system on a local utility company’s call center, voice recognition is critical. It reduces friction for users and even increases accessibility to a wider population.

3. Virtual personal assistants

Whether you’re talking to Siri, Alexa, or Google, virtual assistants use machine learning to get better at giving you answers. These services use speech recognition technology, but they’re also using machine learning to capture data on what you’re asking for, when, and how often they get it “right.” Machine learning utilizes all of these data sets to improve the services provided and helps inform and guide the companies’ decision-making.

4. Customer service reps

When the little chat box pops up next time you’re shopping online, the “person” who answers might not be a person at all. Many companies have switched to using chatbots that deploy conversational AI to answer customers’ questions. These AI use machine learning to improve their understanding of customers’ responses and answers. Whether the input is voice or text, Machine Learning Engineers have plenty of work to improve bot conversations for companies worldwide.

5. Social media algorithms

This one probably comes as no surprise. People talk about “the algorithm” all the time.

Think about all the data captured on your social media account — what you like, the posts you engage with, the times of day you’re most active, what ads you’ll click on, and more. Machine-learning algorithms use all that information to customize your social media feeds and better market to you.

6. Fraud detection

When your credit card use seems a little different than usual, a machine learning algorithm can flag it for you. Rather than having people investigate strange occurrences manually, machine learning builds a model of your spending and can even temporarily freeze accounts when it predicts you’re not the one doing the spending.

7. Streaming recommendations

Ever wonder how Netflix seems to know just the right show to recommend? It’s because they too use machine learning to suggest your next binge-watch based on your previous watch history. Similarly, Spotify will pull together suggested playlists based on your listening preferences, and YouTube suggests related videos to the one you just watched. While much of it can be marketing, it tailors the customer experience and makes it better for all.

8. Traffic predictions

Whenever Google Maps (or your preferred navigation system) gives you an estimated time of arrival, it’s using machine learning to predict your trip’s duration. First, Google uses machine learning to build a model of how long certain trips take based on historical traffic data. Then, it uses that data based on your current trip and traffic levels to predict how long it’ll take to arrive at your destination. They’ve even partnered with DeepMind to further improve their graph neural networks.

9. Analyzing medical imaging

Radiologists and doctors need to analyze a monumental number of scans. This often leaves them tired, which can sometimes lead to errors. Fortunately, machine learning can help.

Machines can be trained to analyze medical imaging (like CT scans and MRIs) to identify any anomalies. For example, the technology developed by Infervision uses machine learning to diagnose cancer in patients more accurately. It’s an impactful way to put image recognition to task in service of improving healthcare.

How to get started with machine learning

Depending on what you want to do with your machine learning skills, you could take a few different learning approaches. Since machine learning is a subfield of data science, you’ll want to start by learning programming languages that are popularly used in the field. These languages include Python, R, and SQL. Use any of the links below to start learning these languages:

Once you’ve mastered these languages, check out any of the following courses to learn how to use them for machine learning:

If you’re looking for a more cohesive approach, our Data Scientist Career Path might be right for you. It’ll take the guesswork out of what to learn and in what order. It’ll also prepare you for other types of tasks, in addition to machine learning work.