The use of Machine Learning is growing at an accelerated rate. According to a global forecast of MarketsandMarkets, the market value of machine learning will reach around 8.81 billion USD by 2022 that was only 1.03 billion USD in 2016. Most of the people often find themselves using this technology in their lives without even realizing it. Indeed, the use of Google Maps, Alexa, social networking sites and even Siri is all about machine learning.

This post will tell you about the most popular Machine Learning uses and the future of Machine Learning.

The uses of Machine Learning can be varied, and the limit only lies in the creativity of the programmers. Today DigitalCruch brings you the list of the 11 most popular uses of Machine Learning:

1. Image Recognition

The most frequent use of Machine Learning is associated with image recognition. Yes, people can identify images, find new places or recognize objects with the help of ML and AI.

Facebook is a clear example, as it has a function to suggest friends when you are about to tag a photo. In this case, Facebook’s technology uses facial detection and recognition algorithms through Machine Learning.

Face recognition is widely used in the airline industry to detect the presence of criminals and work for the big corporations to pass the employees based on visual identification. To know how to introduce face recognition for your project, check this page.

Image Recognition

2. Voice Recognition

Speech recognition is also another well-known use of a combo of machine learning (ML) and artificial intelligence (AI). Whether you open your smart lock through a verbal command or use Google Assistance or Cortana, it is all about the application of ML.

Voice recognition technology has been one of the fastest-growing uses of Machine Learning in the last decade. Statista also anticipates that the global market size of the voice recognition industry will reach up to 27.16 billion USD by 2026. This figure was only 10.7 billion USD in 2020.

Voice Recognition

3. Traffic Prediction

Do you usually depend on Google Maps to travel from one place to another place or send them ‘pin locations’ to share your current location? If yes, then it is also a perfect example of using AI and ML to activate GPS technology. Yes, GPS technology and Machine Learning have merged to provide a better service with digital maps. Nowadays, apps can predict traffic and suggest the best route to get there in less time.

Applications such as Google Maps, Waze, and other digital maps collect real-time information from users and process it to help the application and users find the most optimal route.

Traffic Prediction

4. Product Recommendation

Have you ever utilized an OTT or streaming platform or purchased something online? Companies like Alibaba, Netflix, YouTube, Hulu or Amazon trust Machine Learning algorithms to improve their search recommendations.

When you search for something, consume content or buy something, the sites collect information from your interactions to process it and feed it to a Machine Learning algorithm, which will recommend something for your next interaction.

It’s not just products; Google uses recommendation algorithms to offer a better search experience.

Product Recommendation Machine Learning

5. Virtual Assistants

This guide has already mentioned virtual assistants in the speech recognition point, but the use of Machine Learning in virtual assistants encompasses much more than just speech recognition.

The construction of a virtual assistant can be as complex as its developer wants. Using different Machine Learning algorithms results in a better experience, suggesting songs, improving your schedule, and even taking a call for you through smart home devices.

Virtual Assistance

6. Language Translations

Google Neural Machine Translation is a Machine Learning program that translates text from almost any language to another. We know it better as Google translate.

More companies have their algorithms for translation. Millions of people use these services. Many of us, often use as a paper writer free and paid translation or proofreading services, such as DeepL Translator or Grammarly. There are even browsers already with the function of translating by Machine Learning and can translate entire sites into your language. Fascinating, isn’t it?

Language Translations

7. Autonomous Vehicles

We are not so far from living in a science fiction movie; one of the most exciting uses of Machine Learning can be found in autonomous vehicles. Likewise, renowned electric car manufacturer company Tesla also claimed about using ML to enhance their self-driving features.

Autonomous Vehicles

8. Email Filters

How do the email service providers know what is important and what is spam? When we receive an email, the email goes through an automatic filter to reach our inbox, while other emails go straight to spam.

Thanks to different Machine Learning algorithms such as decision tree algorithms or Bayesian algorithms, spam and malware detection is detected in time to avoid filling our mailbox with junk.

Email Filters

9. Medical Diagnostics

One of the most promising uses of machine learning is in the medical field. The diagnosis of diseases with the help of Machine Learning is in very accelerated growth. There are already solutions to build 3D models to find the exact position in the brain.

Machine Learning technology to locate brain lesions is helping to find brain tumours and other types of brain-related diseases more easily.

Medical Diagnostics

10. Personalized Marketing

Marketing strategies have always had one goal: to know more about the customer to serve their needs and sell more.

Machine Learning technology has helped many businesses achieve their goals. Digital ad platforms use Machine Learning to create better strategies and techniques to find the right customers.

Also, Machine Learning technology in marketing sends personalized emails and recommendations.

Personalized Marketing

11. Fraud Detection

Machine Learning is a key part of banking security since one of the most popular uses of Machine Learning in the financial industry is the detection of fraudulent transactions.

Fraud detection with Machine Learning is possible thanks to recognizing fake accounts, stolen IDs, and even identifying stolen money transactions.

Fraud Detection

The Future of Machine Learning

The applications of Machine Learning that were shown to you were unfathomable just a few years ago. Still, with the rate of technology innovation and scientific discoveries, we are continually astounded.

So, what is the next step, and what is the future of Machine Learning? Machine Learning’s future tendencies are efficiency, security, and less data utilization.

More Efficient Machine Learning

However, ML and AI still have much room for improvement. Fast-paced advances will allow us to see Machine Learning optimized to be more efficient in a few years.

The trend is to implement artificial intelligence to recognize its mistakes and improve based on them without human supervision.

Protecting Against Cyberattacks with Machine Learning

As technology advances, so does cybercrime, forcing companies to reevaluate their processes for protecting online systems. Artificial intelligence and Machine Learning represent key pieces to monitor, prevent and respond to cyber-attacks. One day, database theft and digital fraud will probably be a thing of the past.

Less Data for Machine Learning

As machines have more processing power and become more efficient, Machine Learning needs less data to achieve its programmed function.

The trend is towards training Machine Learning models with less data, which will result in models that learn with much smaller data sets.

Hopefully, this article will assist our readers in thinking or working more passionately about using ML and AI approaches to introduce something new. You can also share your thoughts about Machine Learning and its uses by commenting below!

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