Development9 min read

10 machine learning examples of apps we use every day - profile photo

Jakub Kluczewski

Java Developer - profile photo

Alexa Trachim

Technical Content Writer

10 machine learning examples of apps we use every day

Machine learning is a quite appealing topic for our clients and ourselves. Learning algorithms can be applied to many digitalized activities and can be an integral part of the systems we use daily. The power offered by computers with machine learning capabilities is undeniable. Furthermore, we already use them – or should we rather say we use products based on these algorithms. They are present in many areas of online life – in business, entertainment and domestic life.

What is machine learning?

Before we present examples of machine learning applications and systems, let us give you a short introduction to the concept.

Machine learning is a part of artificial intelligence (AI) science. The whole area of artificial intelligence studies is based on the idea of creating computers that would obtain human-like thinking abilities. And machine learning algorithms are responsible for learning and concluding predictions based on provided data.

Thanks to machine learning, computers can discover patterns and find dependencies that could be missed by the human eye. That is done with advanced algorithms that can analyze significant amounts of data and see similarities. There are many types of machine learning, one of them being deep learning based on neural networks inspired by the human brain.

Now you probably can tell what machine learning could be used for. We will provide a couple of examples to show you how diverse and sophisticated machine learning algorithms can be.

If you want to read more about machine learning, deep learning and artificial intelligence, check out our article on this growing trend. We explain there how all of these algorithms work, what are the differences between them and how each type of machine learning works. It’s a guide for everyone who wants to learn more about this subject.

What is machine learning good for?

What is machine learning good for?

We will provide you with 10 very elaborate machine learning examples. The algorithms are used within these industries and areas in many ways. For example, you can see how often artificial intelligence in the form of machine learning can be useful in only one type of products and apps.

Speech recognition & Image recognition

Computer speech recognition converts natural language we speak into text so that it can be used in voice searches. These interfaces allow voice commands and in voice bots that are used for customer care and booking appointments. Natural language processing is definitely one of the machine learning trends that is continuously growing and becomes more popular.

As for image recognition, which is one of the most popular usages of machine learning, the computer analyzes every pixel of the picture it is provided with. This way, it can, for example, be a part of face recognition mechanisms.

In both of these recognition systems, the way of working is similar – the machine breaks speech or image into smaller pieces. It compares them with information it was programmed with before, looking for the same features. Then, it decides if there’s a match or not and classifies sounds or pixels to the right category.

Big data

Artificial intelligence and big data are an obvious connection. In data science, analysis, organization and concluding from vast amounts of data is crucial for many decisions. Big data becomes a part of more industries every year, as more and more companies need to process enormous amounts of information about their operations, customers, sales and shipments. And machine learning is an undisputed ally when it comes to big data computing.

Communication with machines

And that comes in many shapes and forms. We’ve mentioned voice bots, but chatbots are also based on machine learning and highly utilized in many companies as assistants in brand-customer communication. A chatbot is programmed to answer written text with concrete and suitable info. It used to be quite counterintuitive, but now thanks to machine learning, chatbots can hold conversations similar to the ones between two people.

Coming back to voice communication for a bit. Virtual assistants and every smart speaker like Amazon Echo, Google Home, or Siri are also based on machine learning – not only to recognize the voices of their owners but also to curate better answers based on the user’s preferences.

Social Media platforms

Another example of services that use the machine learning algorithm in many different contexts is Facebook, Twitter, Pinterest and other social media apps. Face recognition we already talked about is one of the examples. But also all types of recommendations – people you may know and should invite to your circle of friends or pins you might like and should add to one of your boards.

Machine learning is also responsible for algorithms that customize our feeds. Back in the day, social media showed us every piece of content in chronological order, but now they do it based on our previous activities. If we like or comment on someone’s pictures more often, this person will appear on top of our feed. If we ignore someone’s posts – they won’t show up anymore. That’s why, for influencers and content creators, it is essential to produce exciting entries, so their followers and random people that stumble upon them will interact with them.


Machine learning applications in healthcare are mostly used to diagnose patients and detect diseases. The computer can analyze scans, test results and medical records not only to find out what are the current illnesses but also to predict the state of health in the future. Therapy planning and giving recommendations to improve the current lifestyle is much more detailed and concrete.


Machine learning in the financial sector can be used in two ways – to help with trading strategies and to prevent fraudulent behaviors.

A trading algorithm is a tool that every stock market and currency trading fan will appreciate. Thanks to machine learning, comparing statistics of assets helps with arbitrage, which is a short-term trading strategy. Analyzing historical data and information about the present economic situation produces predictions that can support financial decisions. Machine learning also helps with creating tailor-made investment offers and other banking products.

Money laundering and fraud detection can be automatized with machine learning. Their effectiveness will be much better because the computer can find patterns in behaviors that are suspicious.


Machine learning examples in the retail industry are significant. It can improve the shopping experience of all the customers by offering them product recommendations suited to their preferences and requirements. That is a marketing strategy that can really grow sales and attract more potential, faithful clients.

Also, in the world of ecommerce transactions, customer service is essential. It can be automatized with chatbots and voice bots, as we mentioned above. Initial verifications of the complaints, doing satisfactory research, collecting data on sales and drawing conclusions that can help with growing revenue – there are countless possibilities.


We’ve already mentioned a couple of ways to secure financial transactions and safely process huge amounts of data. But machine learning can be useful also when it comes to automatizing physical security systems.

A video surveillance system usually was monitored by one person, which was not efficient and caused lots of mistakes. With machine learning, controlling cameras and what they see 24/7 is easy. Systems based on ML algorithms are analyzing unusual behaviors on the screen and alert people responsible for the security of the object. This way, they can early prevent any crime or misconduct.


Machine learning in emails is usually used for spam filtering. And that happens not only by teaching the prediction systems what kind of messages can be malware. Modern spam detection also tries to anticipate the spammers and find out beforehand what kind of tricks they will use to pass by the spam filtering application. 

Search engines

Every search engine, for example, Google, Bing, or Yahoo, but also internal search engines that are a part of social media platforms and other apps, use machine learning. That’s because search engine results are based on our past choices when it comes to picking and visiting websites displayed by the engine. This way, the machine learning technology takes care of always showing us results that will be really interesting for us.

So, where is machine learning used?

The answer is – almost everywhere! Businesses that use it are present in practically every industry and area of our lives – even fashion, food, or beauty. We usually associate artificial intelligence with robots, but it can be much more earthbound than we might imagine. And that doesn’t mean it is not significant for our daily lives. Quite contrary – without machine learning, we wouldn’t have a chance to do a lot of things online, or they would take a lot of time and sweat to accomplish.

In one of the articles on our blog, we already discussed machine learning as a growing trend because we believe this technology can change people’s lives on many different levels. In the future, machine learning will probably be a part of every system that requires data analytics. Its role will be the classification of this data to create comprehensive suggestions and instructions to approach. The goal? To achieve success and give customers what they really need.

Will your business be the next example of successful machine learning implementation?

If this article encouraged you to go deeper and try a machine learning model for your business – don’t hesitate to let us know what you have in mind. You can become the next example that will inspire other brands to try this modern technology for their operations and processes. And we will gladly answer all of your questions and help you to invite machine learning to your company. It is a must-have if you produce and analyze lots of data, but not only. Contact us so we can start our cooperation ASAP!

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    Jakub Kluczewski

    Java Developer - profile photo

    Alexa Trachim

    Technical Content Writer

    Post article

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