User personalization or natural language processing (NLP) are all examples of machine learning (ML). We’ve already got used to a big number of applications that help us in our daily lives. Think of Siri on your phone or Netflix on your TV, recommendations on Instagram, or a chatbot in your online banking. They all use machine learning technologies.
While ML continues to evolve, businesses take steps further to implement it in their applications. Let’s see how machine learning actually works, which machine learning examples in real life exist, and how they transform our lives.
Machine learning is a division of artificial intelligence that allows building models that learn from experience. If you need to create an application based on machine learning, you’ll need to hire ML engineers. They will create algorithms that enable computers to make decisions based on the provided data during the training process.
Before you start using machine learning real life examples, they go through a number of steps:
- Data collection. You define the task and provide the data which the ML algorithms will learn.
- Data classification. Before you train the ML model, the data should be cleared, classified, and structured. This makes the learning process smoother.
- Model training. Whether you choose to implement neural networks models (such as large language models) or decision trees, the model goes through the training process. During the training, the ML algorithms learn to identify the patterns and understand the relationships between the input and the output.
- Decision-making. After the training, the model provides the output, or the prediction. The outcome will depend on the data you feed for training, as well as the initial parameters you set up.
- Implementation. After the final outputs have been validated, the ML model is good to go, and you pass to the deployment of examples of machine learning in everyday life.
With deep learning technology and involvement of neural networks that continue to advance, machine learning can continually improve with feedback and modified parameters. Thanks to these possibilities, a number of ML applications continues to grow.
At work or in private life, outside or at home, we use machine learning algorithms every day without even noticing. They save us time and make our life easier and more comfortable. Let’s see which examples of machine learning in everyday life we use the most:
- Spam mails. You have probably noticed how smart our mailboxes have become, filtering spam mails automatically and transferring them to spam folders. This has become possible thanks to machine learning algorithms that analyze the content and categorize mails into the relevant folder.
- Text prediction and autocorrection. Whether you type a message in Facebook Messenger or use Outlook for your work, autocorrection and prediction features exist everywhere. They make our messages more accurate and save us time suggesting the next words.
- Face recognition. Thanks to ML learning algorithms, we unblock our smartphones every day and see tagged friends on our photos.
- Virtual Personal Assistants (VPAs). There are for sure those who delegate their daily tasks to Siri, Alexa, or Google Assistant. These helpers are all built on machine learning algorithms, which allows them to respond to your queries and accomplish simple tasks.
- Recommendation systems. The most popular machine learning examples in real life include online recommendation algorithms that we meet on every corner. They are used by well-known Netflix, Spotify, and Amazon, and exist on almost every site. As soon as you purchase something or add it to your wishlist, the algorithm offers you something similar based on your preferences.
- Traffic analysis. Traffic control is one of the most widespread examples of ML usage. Thanks to it, we see traffic on roads, receive information on weather conditions, and optimize our everyday travels.
- Chatbots. As one of the popular machine learning real life examples, a chatbot takes a leading role in automation of customer operations. From banking to retail, from communication services to B2B operations, you have seen the application of bots in almost every sphere.
- Linux/Unix application. Machine learning algorithms proved their efficiency in optimizing system performance and detecting security breaches. They help to analyze resources allocation, optimize them, and improve the overall settings. In the field of security, the ML helps to identify any anomalies in network and traffic. This allows businesses working on Linux systems to proactively eliminate any security threats.
This list of examples is not exhaustive, and the application of ML conquers more and more industries, including finances, healthcare, and retail. More businesses see the exciting opportunities the algorithms offer and decide to invest in the modernization of their processes.
The usage of machine learning algorithms has brought the interaction with the customer to a new level. Their implementation into business applications creates a better customer experience, benefitting both businesses and customers themselves.
As individuals, we enjoy new features and technologies that make our lives easier. As businesses, we upgrade our applications and sites to attract even more customers. The intuitive approach and seamless interaction between different channels of communication is what reigns in today’s landscape.
Customers want to get a quick and user-friendly experience, while businesses want their clients to stay loyal. Both sides get win-win results in the end.
Due to high competition, leading a business today without involving new algorithms and technologies is the path that would hardly lead to success. The application of artificial intelligence, and machine learning algorithms in particular, has changed the way we interact and do our business. They dictate the rules, but they also give us endless opportunities to explore.
Machine learning algorithms transformed our lives for the better. They penetrated our daily lives, our work, and our interaction with each other, offering simpler solutions, saving our time, and helping with daily tasks. From word analysis and prediction to personalized recommendations and traffic control, machine learning provides us with enhanced user experience in a dynamic technology-driven world. They remain one of the main forces for an innovative future and the discovery of our limits.